Stephen Boyd
Samsung Professor in the School of Engineering
Electrical Engineering
Web page: http://web.stanford.edu/~boyd
Bio
Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University, and a member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
He has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. In 2023, he was given the AACC Richard E. Bellman Control Heritage Award, the highest recognition of professional achievement for U.S. control systems engineers and scientists. He is a Fellow of the IEEE, SIAM, INFORMS, and IFAC, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education. In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization."
Academic Appointments
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Professor, Electrical Engineering
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Member, Bio-X
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
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Affiliate, Precourt Institute for Energy
Administrative Appointments
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Chair, Department of Electrical Engineering (2018 - Present)
Honors & Awards
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Beal-Orchard-Hays Prize, Mathematical Optimization Society (2024)
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Richard E. Bellman Control Heritage Award, American Automatic Control Council (2023)
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Fellow, International Federation of Automatic Control (2022)
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Foreign member, National Academy of Engineering of Korea (2020)
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Athanasios Papoulis Society Award, European Association for Signal Processing (EURASIP) (2019)
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Foreign member, Chinese Academy of Engineeering (2017)
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Honorary PhD, University Catholique de Louvain (2017)
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James H. Mulligan, Jr. Education Medal, IEEE (2017)
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Fellow, INFORMS (2016)
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Walter J. Gores teaching award, Stanford (2016)
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Fellow, SIAM (2015)
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Member, National Academy of Engineering (2014)
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Saul Gass Award, INFORMS (2014)
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Control Systems Award, IEEE (2013)
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Beal-Orchard-Hays Prize, Mathematical Optimization Society (2012)
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Honorary PhD, Royal Institute of Technology (KTH), Stockholm (2006)
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Section lecture, International Congress of Mathematicians (2006)
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John R. Ragazzini Award, Automatic Control Council (2003)
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Fellow, IEEE Control Systems Society (1999)
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Hugo Schuck Award, IEEE Control Systems Society (1999)
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Perrin Award for Undergraduate Teaching, Stanford University (1994)
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Distinguished Lecturer, IEEE Control System Society (1993)
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Donald P. Eckman Award, IEEE Control Systems Society (1992)
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Graduate Teaching Award, ASSU (1991)
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Presidential Young Investigator Award, National Science Foundation (1986)
Professional Education
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PhD, UC Berkeley, EECS (1985)
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BA, Harvard University, Mathematics (1980)
2024-25 Courses
- Convex Optimization I
CME 364A, EE 364A (Win) -
Independent Studies (20)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Curricular Practical Training
CME 390 (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Directed Research and Writing in Aero/Astro
AA 190 (Win, Spr) - Independent Project
CS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399P (Aut, Win, Spr, Sum) - Independent Study in Aero/Astro
AA 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Master's Thesis and Thesis Research
EE 300 (Aut, Win, Spr, Sum) - Part-time Curricular Practical Training
CS 390D (Aut, Win, Spr, Sum) - Ph.D. Research
CME 400 (Aut, Win, Spr, Sum) - Senior Project
CS 191 (Aut, Win, Spr, Sum) - Special Studies and Reports in Electrical Engineering
EE 191 (Aut, Win, Spr, Sum) - Special Studies and Reports in Electrical Engineering
EE 391 (Aut, Win, Spr, Sum) - Special Studies and Reports in Electrical Engineering (WIM)
EE 191W (Aut, Win, Spr, Sum) - Special Studies or Projects in Electrical Engineering
EE 190 (Aut, Win, Spr, Sum) - Special Studies or Projects in Electrical Engineering
EE 390 (Aut, Win, Spr, Sum) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2023-24 Courses
- Convex Optimization I
CME 364A, EE 364A (Win) - Introduction to Machine Learning
CME 107, EE 104 (Spr)
2022-23 Courses
- Convex Optimization I
CME 364A, EE 364A (Win)
2021-22 Courses
- Convex Optimization I
CME 364A, EE 364A (Win) - Introduction to Matrix Methods
ENGR 108 (Aut)
- Convex Optimization I
All Publications
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Approximate sequential optimization for informative path planning
ROBOTICS AND AUTONOMOUS SYSTEMS
2024; 182
View details for DOI 10.1016/j.robot.2024.104814
View details for Web of Science ID 001333214800001
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Efficient Shapley performance attribution for least-squares regression
STATISTICS AND COMPUTING
2024; 34 (5)
View details for DOI 10.1007/s11222-024-10459-9
View details for Web of Science ID 001262282900001
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Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2024
View details for DOI 10.1007/s10957-024-02491-6
View details for Web of Science ID 001283260400001
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Markowitz Portfolio Construction at Seventy
JOURNAL OF PORTFOLIO MANAGEMENT
2024; 50 (8): 117-160
View details for Web of Science ID 001272825200011
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Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2024
View details for DOI 10.1007/s10957-024-02436-z
View details for Web of Science ID 001220929000001
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Polyak Minorant Method for Convex Optimization
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2024
View details for DOI 10.1007/s10957-024-02412-7
View details for Web of Science ID 001194609600001
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Portfolio Optimization with Cumulative Prospect Theory Utility via Convex Optimization
COMPUTATIONAL ECONOMICS
2024
View details for DOI 10.1007/s10614-024-10556-x
View details for Web of Science ID 001159490000001
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Compact Model Parameter Extraction via Derivative-Free Optimization
IEEE ACCESS
2024; 12: 123224-123235
View details for DOI 10.1109/ACCESS.2024.3453198
View details for Web of Science ID 001311188600001
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Fast Path Planning Through Large Collections of Safe Boxes
IEEE TRANSACTIONS ON ROBOTICS
2024; 40: 3795-3811
View details for DOI 10.1109/TRO.2024.3434168
View details for Web of Science ID 001291896800003
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Implementation of an oracle-structured bundle method for distributed optimization
OPTIMIZATION AND ENGINEERING
2023
View details for DOI 10.1007/s11081-023-09859-z
View details for Web of Science ID 001109181100001
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Value-gradient iteration with quadratic approximate value functions
ANNUAL REVIEWS IN CONTROL
2023; 56
View details for DOI 10.1016/j.arcontrol.2023.100917
View details for Web of Science ID 001112281100001
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Portfolio construction with Gaussian mixture returns and exponential utility via convex optimization
OPTIMIZATION AND ENGINEERING
2023
View details for DOI 10.1007/s11081-023-09814-y
View details for Web of Science ID 001037322900001
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Bounds on Efficiency Metrics in Photonics
ACS PHOTONICS
2023
View details for DOI 10.1021/acsphotonics.3c00023
View details for Web of Science ID 001024824200001
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Convex optimization over risk-neutral probabilities
OPTIMIZATION AND ENGINEERING
2023
View details for DOI 10.1007/s11081-023-09802-2
View details for Web of Science ID 000976821600001
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PV Fleet Modeling via Smooth Periodic Gaussian Copula
IEEE. 2023
View details for DOI 10.1109/PVSC48320.2023.10359991
View details for Web of Science ID 001151676200467
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Tractable Evaluation of Stein's Unbiased Risk Estimate With Convex Regularizers
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2023; 71: 4330-4341
View details for DOI 10.1109/TSP.2023.3323046
View details for Web of Science ID 001123968900002
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Signal Decomposition Using Masked Proximal Operators
FOUNDATIONS AND TRENDS IN SIGNAL PROCESSING
2023; 17 (1): 1-78
View details for DOI 10.1561/200000122
View details for Web of Science ID 000994685200001
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RSQP: Problem-specific Architectural Customization for Accelerated Convex Quadratic Optimization
ASSOC COMPUTING MACHINERY. 2023: 1026-1037
View details for DOI 10.1145/3579371.3589108
View details for Web of Science ID 001098723900073
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Confidence Bands for a Log-Concave Density
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
2022; 31 (4): 1426-1438
View details for DOI 10.1080/10618600.2022.2076688
View details for Web of Science ID 000888844200039
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Computing tighter bounds on the n-queens constant via Newton's method
OPTIMIZATION LETTERS
2022
View details for DOI 10.1007/s11590-022-01933-2
View details for Web of Science ID 000854857100001
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Covariance prediction via convex optimization
OPTIMIZATION AND ENGINEERING
2022
View details for DOI 10.1007/s11081-022-09765-w
View details for Web of Science ID 000849470800001
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A general optimization framework for dynamic time warping
OPTIMIZATION AND ENGINEERING
2022
View details for DOI 10.1007/s11081-022-09738-z
View details for Web of Science ID 000842420700001
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Multi-period liability clearing via convex optimal control
OPTIMIZATION AND ENGINEERING
2022
View details for DOI 10.1007/s11081-022-09737-0
View details for Web of Science ID 000819912100001
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Confidence Bands for a Log-Concave Density
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
2022
View details for DOI 10.1080/01621459.2022.2076688
View details for Web of Science ID 000815471000001
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Fitting feature-dependent Markov chains
JOURNAL OF GLOBAL OPTIMIZATION
2022
View details for DOI 10.1007/s10898-022-01198-0
View details for Web of Science ID 000811964900001
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Operator splitting for adaptive radiation therapy with nonlinear health dynamics
OPTIMIZATION METHODS & SOFTWARE
2022
View details for DOI 10.1080/10556788.2022.2078824
View details for Web of Science ID 000804607500001
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Allocation of fungible resources via a fast, scalable price discovery method
MATHEMATICAL PROGRAMMING COMPUTATION
2022
View details for DOI 10.1007/s12532-022-00220-6
View details for Web of Science ID 000784049700001
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Minimizing oracle-structured composite functions
OPTIMIZATION AND ENGINEERING
2022
View details for DOI 10.1007/s11081-021-09705-0
View details for Web of Science ID 000741580400001
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Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization
IEEE ACCESS
2022; 10: 115384-115398
View details for DOI 10.1109/ACCESS.2022.3218802
View details for Web of Science ID 000880586800001
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PORTFOLIO PERFORMANCE ATTRIBUTION VIA SHAPLEY VALUE
JOURNAL OF INVESTMENT MANAGEMENT
2022; 20 (3): 33-52
View details for Web of Science ID 000834737700004
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Strategic Asset Allocation with Illiquid Alternatives
ASSOC COMPUTING MACHINERY. 2022: 249-256
View details for DOI 10.1145/3533271.3561769
View details for Web of Science ID 001103234000030
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A Certainty Equivalent Merton Problem
IEEE CONTROL SYSTEMS LETTERS
2022; 6: 1478-1483
View details for DOI 10.1109/LCSYS.2021.3111534
View details for Web of Science ID 000697816400001
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Stochastic Control With Affine Dynamics and Extended Quadratic Costs
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2022; 67 (1): 320-335
View details for DOI 10.1109/TAC.2021.3064535
View details for Web of Science ID 000735567400026
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Embedded Code Generation With CVXPY
IEEE CONTROL SYSTEMS LETTERS
2022; 6: 2653-2658
View details for DOI 10.1109/LCSYS.2022.3173209
View details for Web of Science ID 000797431900004
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Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2021; 68 (10): 2907-2917
Abstract
Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front.Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM).On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk.Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners.Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.
View details for DOI 10.1109/TBME.2021.3055822
View details for Web of Science ID 000697820800006
View details for PubMedID 33523802
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Learning Convex Optimization Models
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
2021; 8 (8): 1355-1364
View details for DOI 10.1109/JAS.2021.1004075
View details for Web of Science ID 000669496100001
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Convex restrictions in physical design.
Scientific reports
2021; 11 (1): 12976
Abstract
In a physical design problem, the designer chooses values of some physical parameters, within limits, to optimize the resulting field. We focus on the specific case in which each physical design parameter is the ratio of two field variables. This form occurs for photonic design with real scalar fields, diffusion-type systems, and others. We show that such problems can be reduced to a convex optimization problem, and therefore efficiently solved globally, given the sign of an optimal field at every point. This observation suggests a heuristic, in which the signs of the field are iteratively updated. This heuristic appears to have good practical performance on diffusion-type problems (including thermal design and resistive circuit design) and some control problems, while exhibiting moderate performance on photonic design problems. We also show in many practical cases there exist globally optimal designs whose design parameters are maximized or minimized at each point in the domain, i.e., that there is a discrete globally optimal structure.
View details for DOI 10.1038/s41598-021-92451-1
View details for PubMedID 34155295
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Dirty Pixels: Towards End-to-end Image Processing and Perception
ACM TRANSACTIONS ON GRAPHICS
2021; 40 (3)
View details for DOI 10.1145/3446918
View details for Web of Science ID 000695551400004
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Extracting a low-dimensional predictable time series
OPTIMIZATION AND ENGINEERING
2021
View details for DOI 10.1007/s11081-021-09643-x
View details for Web of Science ID 000655571500001
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Fitting Laplacian regularized stratified Gaussian models
OPTIMIZATION AND ENGINEERING
2021
View details for DOI 10.1007/s11081-021-09611-5
View details for Web of Science ID 000633757100001
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Tax-Aware Portfolio Construction via Convex Optimization
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2021
View details for DOI 10.1007/s10957-021-01823-0
View details for Web of Science ID 000621695700002
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Optimal representative sample weighting
STATISTICS AND COMPUTING
2021; 31 (2)
View details for DOI 10.1007/s11222-021-10001-1
View details for Web of Science ID 000622716900001
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Heuristic methods and performance bounds for photonic design
OPTICS EXPRESS
2021; 29 (2): 2827–54
View details for DOI 10.1364/OE.415052
View details for Web of Science ID 000609227300184
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Eigen-stratified models
OPTIMIZATION AND ENGINEERING
2021
View details for DOI 10.1007/s11081-020-09592-x
View details for Web of Science ID 000605507300002
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Minimum-Distortion Embedding
FOUNDATIONS AND TRENDS IN MACHINE LEARNING
2021; 14 (3): 211-378
View details for DOI 10.1561/2200000090
View details for Web of Science ID 000695538200001
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A Distributed Method for Fitting Laplacian Regularized Stratified Models
JOURNAL OF MACHINE LEARNING RESEARCH
2021; 22
View details for Web of Science ID 000656358900001
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Sample Efficient Reinforcement Learning with REINFORCE
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2021: 10887-10895
View details for Web of Science ID 000681269802065
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CVXR: An R Package for Disciplined Convex Optimization
JOURNAL OF STATISTICAL SOFTWARE
2020; 94 (14)
View details for DOI 10.18637/jss.v094.i14
View details for Web of Science ID 000571010700001
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Bounds for Scattering from Absorptionless Electromagnetic Structures
PHYSICAL REVIEW APPLIED
2020; 14 (1)
View details for DOI 10.1103/PhysRevApplied.14.014025
View details for Web of Science ID 000615674000003
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Automatic repair of convex optimization problems
OPTIMIZATION AND ENGINEERING
2020
View details for DOI 10.1007/s11081-020-09508-9
View details for Web of Science ID 000535100000001
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Least squares auto-tuning
ENGINEERING OPTIMIZATION
2020
View details for DOI 10.1080/0305215X.2020.1754406
View details for Web of Science ID 000532206800001
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SWIFTCORE: a tool for the context-specific reconstruction of genome-scale metabolic networks.
BMC bioinformatics
2020; 21 (1): 140
Abstract
BACKGROUND: High-throughput omics technologies have enabled the comprehensive reconstructions of genome-scale metabolic networks for many organisms. However, only a subset of reactions is active in each cell which differs from tissue to tissue or from patient to patient. Reconstructing a subnetwork of the generic metabolic network from a provided set of context-specific active reactions is a demanding computational task.RESULTS: We propose SWIFTCC and SWIFTCORE as effective methods for flux consistency checking and the context-specific reconstruction of genome-scale metabolic networks which consistently outperform the previous approaches.CONCLUSIONS: We have derived an approximate greedy algorithm which efficiently scales to increasingly large metabolic networks. SWIFTCORE is freely available for non-commercial use in the GitHub repository at https://mtefagh.github.io/swiftcore/.
View details for DOI 10.1186/s12859-020-3440-y
View details for PubMedID 32293238
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Minimizing a sum of clipped convex functions
OPTIMIZATION LETTERS
2020
View details for DOI 10.1007/s11590-020-01565-4
View details for Web of Science ID 000521663800002
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Disciplined quasiconvex programming
OPTIMIZATION LETTERS
2020
View details for DOI 10.1007/s11590-020-01561-8
View details for Web of Science ID 000517724900001
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Network optimization for unified packet and circuit switched networks
OPTIMIZATION AND ENGINEERING
2020; 21 (1): 159–80
View details for DOI 10.1007/s11081-019-09439-0
View details for Web of Science ID 000516560400007
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OSQP: an operator splitting solver for quadratic programs
MATHEMATICAL PROGRAMMING COMPUTATION
2020
View details for DOI 10.1007/s12532-020-00179-2
View details for Web of Science ID 000517229100002
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ON THE CONVERGENCE OF MIRROR DESCENT BEYOND STOCHASTIC CONVEX PROGRAMMING
SIAM JOURNAL ON OPTIMIZATION
2020; 30 (1): 687–716
View details for DOI 10.1137/17M1134925
View details for Web of Science ID 000546998300026
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ANDERSON ACCELERATED DOUGLAS-RACHFORD SPLITTING
SIAM JOURNAL ON SCIENTIFIC COMPUTING
2020; 42 (6): A3560–A3583
View details for DOI 10.1137/19M1290097
View details for Web of Science ID 000600650400012
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GLOBALLY CONVERGENT TYPE-I ANDERSON ACCELERATION FOR NONSMOOTH FIXED-POINT ITERATIONS
SIAM JOURNAL ON OPTIMIZATION
2020; 30 (4): 3170–97
View details for DOI 10.1137/18M1232772
View details for Web of Science ID 000600651900018
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A simple effective heuristic for embedded mixed-integer quadratic programming
INTERNATIONAL JOURNAL OF CONTROL
2020; 93 (1): 2–12
View details for DOI 10.1080/00207179.2017.1316016
View details for Web of Science ID 000576172600003
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VARIABLE METRIC PROXIMAL GRADIENT METHOD WITH DIAGONAL BARZILAI-BORWEIN STEPSIZE
IEEE. 2020: 3597–3601
View details for Web of Science ID 000615970403169
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Fitting a Kalman Smoother to Data
IEEE. 2020: 1526–31
View details for Web of Science ID 000618079801084
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Optimal Operation of a Plug-in Hybrid Vehicle with Battery Thermal and Degradation Model
IEEE. 2020: 3083–90
View details for Web of Science ID 000618079803006
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Solution refinement at regular points of conic problems
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
2019; 74 (3): 627–43
View details for DOI 10.1007/s10589-019-00122-9
View details for Web of Science ID 000495854800002
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Multi-period portfolio selection with drawdown control
SPRINGER. 2019: 245–71
View details for DOI 10.1007/s10479-018-2947-3
View details for Web of Science ID 000490903300011
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Infeasibility Detection in the Alternating Direction Method of Multipliers for Convex Optimization
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2019; 183 (2): 490–519
View details for DOI 10.1007/s10957-019-01575-y
View details for Web of Science ID 000483695300006
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Greedy Gaussian segmentation of multivariate time series
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
2019; 13 (3): 727–51
View details for DOI 10.1007/s11634-018-0335-0
View details for Web of Science ID 000483573200008
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A Distributed Method for Optimal Capacity Reservation
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2019; 182 (3): 1130–49
View details for DOI 10.1007/s10957-019-01528-5
View details for Web of Science ID 000475949900014
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Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2019; 20 (9): 3536–45
View details for DOI 10.1109/TITS.2018.2877572
View details for Web of Science ID 000484207200029
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Disciplined geometric programming
OPTIMIZATION LETTERS
2019; 13 (5): 961–76
View details for DOI 10.1007/s11590-019-01422-z
View details for Web of Science ID 000469798700001
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Real-Time Radiation Treatment Planning with Optimality Guarantees via Cluster and Bound Methods
INFORMS JOURNAL ON COMPUTING
2019; 31 (3): 544–58
View details for DOI 10.1287/ijoc.2018.0841
View details for Web of Science ID 000478965300010
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Computational Bounds for Photonic Design
ACS PHOTONICS
2019; 6 (5): 1232–39
View details for DOI 10.1021/acsphotonics.9b00154
View details for Web of Science ID 000468367600021
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Quantitative flux coupling analysis
JOURNAL OF MATHEMATICAL BIOLOGY
2019; 78 (5): 1459–84
View details for DOI 10.1007/s00285-018-1316-9
View details for Web of Science ID 000463866000009
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A convex optimization approach to radiation treatment planning with dose constraints
OPTIMIZATION AND ENGINEERING
2019; 20 (1): 277–300
View details for DOI 10.1007/s11081-018-9409-2
View details for Web of Science ID 000457786900010
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A convex optimization approach to radiation treatment planning with dose constraints.
Optimization and engineering
2019; 20 (1): 277-300
Abstract
We present a method for handling dose constraints as part of a convex programming framework for inverse treatment planning. Our method uniformly handles mean dose, maximum dose, minimum dose, and dose-volume (i.e., percentile) constraints as part of a convex formulation. Since dose-volume constraints are non-convex, we replace them with a convex restriction. This restriction is, by definition, conservative; to mitigate its impact on the clinical objectives, we develop a two-pass planning algorithm that allows each dose-volume constraint to be met exactly on a second pass by the solver if its corresponding restriction is feasible on the first pass. In another variant, we add slack variables to each dose constraint to prevent the problem from becoming infeasible when the user specifies an incompatible set of constraints or when the constraints are made infeasible by our restriction. Finally, we introduce ConRad, a Python-embedded open-source software package for convex radiation treatment planning. ConRad implements the methods described above and allows users to construct and plan cases through a simple interface.
View details for DOI 10.1007/s11081-018-9409-2
View details for PubMedID 37990749
View details for PubMedCentralID PMC10662894
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Differentiable Convex Optimization Layers
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
View details for Web of Science ID 000535866901022
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Distributed Majorization-Minimization for Laplacian Regularized Problems
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
2019; 6 (1): 45–52
View details for DOI 10.1109/JAS.2019.1911321
View details for Web of Science ID 000455705900003
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Quantitative flux coupling analysis.
Journal of mathematical biology
2018
Abstract
Flux coupling analysis (FCA) aims to describe the functional dependencies among reactions in a metabolic network. Currently studied coupling relations are qualitative in the sense that they identify pairs of reactions for which the activity of one reaction necessitates the activity of the other one, but without giving any numerical bounds relating the possible activity rates. The potential applications of FCA are heavily investigated, however apart from some trivial cases there is no clue of what bottleneck in the metabolic network causes each dependency. In this article, we introduce a quantitative approach to the same flux coupling problem named quantitative flux coupling analysis (QFCA). It generalizes the current concepts as we show that all the qualitative information provided by FCA is readily available in the quantitative flux coupling equations of QFCA, without the need for any additional analysis. Moreover, we design a simple algorithm to efficiently identify these flux coupling equations which scales up to the genome-scale metabolic networks with thousands of reactions and metabolites in an effective way. Furthermore, this framework enables us to quantify the "strength" of the flux coupling relations. We also provide different biologically meaningful interpretations, including one which gives an intuitive certificate of precisely which metabolites in the network enforce each flux coupling relation. Eventually, we conclude by suggesting the probable application of QFCA to the metabolic gap-filling problem, which we only begin to address here and is left for future research to further investigate.
View details for PubMedID 30535964
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Fitting jump models
AUTOMATICA
2018; 96: 11–21
View details for DOI 10.1016/j.automatica.2018.06.022
View details for Web of Science ID 000444659500002
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End-to-end Optimization of Optics and Image Processing for Achromatic Extended Depth of Field and Super-resolution Imaging
ACM TRANSACTIONS ON GRAPHICS
2018; 37 (4)
View details for DOI 10.1145/3197517.3201333
View details for Web of Science ID 000448185000075
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A semidefinite programming method for integer convex quadratic minimization
OPTIMIZATION LETTERS
2018; 12 (3): 499–518
View details for DOI 10.1007/s11590-017-1132-y
View details for Web of Science ID 000429677700006
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Saturating Splines and Feature Selection
JOURNAL OF MACHINE LEARNING RESEARCH
2018; 18
View details for Web of Science ID 000435454900001
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A general system for heuristic minimization of convex functions over non-convex sets
OPTIMIZATION METHODS & SOFTWARE
2018; 33 (1): 165-193
View details for DOI 10.1080/10556788.2017.1304548
View details for Web of Science ID 000429363200008
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Prediction error methods in learning jump ARMAX models
IEEE. 2018: 2247–52
View details for Web of Science ID 000458114802018
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Infeasibility Detection in the Alternating Direction Method of Multipliers for Convex Optimization
IEEE. 2018: 340
View details for Web of Science ID 000454605000071
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OSQP: An Operator Splitting Solver for Quadratic Programs
IEEE. 2018: 339
View details for Web of Science ID 000454605000070
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Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
Cambridge University Press. 2018
View details for DOI 10.1017/9781108583664
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Saturating Splines and Feature Selection.
Journal of machine learning research : JMLR
2018; 18
Abstract
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range. We fit saturating splines to data via a convex optimization problem over a space of measures, which we solve using an efficient algorithm based on the conditional gradient method. Unlike many existing approaches, our algorithm solves the original infinite-dimensional (for splines of degree at least two) optimization problem without pre-specified knot locations. We then adapt our algorithm to fit generalized additive models with saturating splines as coordinate functions and show that the saturation requirement allows our model to simultaneously perform feature selection and nonlinear function fitting. Finally, we briefly sketch how the method can be extended to higher order splines and to different requirements on the extension outside the data range.
View details for PubMedID 31007630
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Embedded Mixed-Integer Quadratic Optimization Using the OSQP Solver
IEEE. 2018: 1536–41
View details for Web of Science ID 000467725301090
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Dynamic Resource Allocation for Energy Efficient Transmission in Digital Subscriber Lines
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2017; 65 (16): 4353-4366
View details for DOI 10.1109/TSP.2017.2711513
View details for Web of Science ID 000404286900015
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Network Inference via the Time-Varying Graphical Lasso.
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
2017; 2017: 205–13
Abstract
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability.
View details for PubMedID 29770256
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Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data.
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
2017; 2017: 215–23
Abstract
Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.
View details for PubMedID 29770257
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Stochastic Matrix-Free Equilibration
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2017; 172 (2): 436-454
View details for DOI 10.1007/s10957-016-0990-2
View details for Web of Science ID 000394266600005
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Linear Convergence and Metric Selection for Douglas-Rachford Splitting and ADMM
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2017; 62 (2): 532-544
View details for DOI 10.1109/TAC.2016.2564160
View details for Web of Science ID 000395510600002
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SnapVX: A Network-Based Convex Optimization Solver
JOURNAL OF MACHINE LEARNING RESEARCH
2017; 18
View details for Web of Science ID 000397018200001
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Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
ASSOC COMPUTING MACHINERY. 2017: 215-223
View details for DOI 10.1145/3097983.3098060
View details for Web of Science ID 000455787300036
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Network Inference via the Time-Varying Graphical Lasso
ASSOC COMPUTING MACHINERY. 2017: 205-213
View details for DOI 10.1145/3097983.3098037
View details for Web of Science ID 000455787300035
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Stochastic Mirror Descent in Variationally Coherent Optimization Problems
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2017
View details for Web of Science ID 000452649407013
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Disciplined Multi-Convex Programming
IEEE. 2017: 895–900
View details for Web of Science ID 000427082203091
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Dynamic Energy Management with Scenario-Based Robust MPC
IEEE. 2017: 2042–47
View details for Web of Science ID 000427033302017
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Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields.
Proceedings of machine learning research
2017; 54: 1302–10
Abstract
Markov random fields (MRFs) are a useful tool for modeling relationships present in large and high-dimensional data. Often, this data comes from various sources and can have diverse distributions, for example a combination of numerical, binary, and categorical variables. Here, we define the pairwise exponential Markov random field (PE-MRF), an approach capable of modeling exponential family distributions in heterogeneous domains. We develop a scalable method of learning the graphical structure across the variables by solving a regularized approximated maximum likelihood problem. Specifically, we first derive a tractable upper bound on the log-partition function. We then use this upper bound to derive the group graphical lasso, a generalization of the classic graphical lasso problem to heterogeneous domains. To solve this problem, we develop a fast algorithm based on the alternating direction method of multipliers (ADMM). We also prove that our estimator is sparsistent, with guaranteed recovery of the true underlying graphical structure, and that it has a polynomially faster runtime than the current state-of-the-art method for learning such distributions. Experiments on synthetic and real-world examples demonstrate that our approach is both efficient and accurate at uncovering the structure of heterogeneous data.
View details for PubMedID 30931433
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Embedded Code Generation Using the OSQP Solver
IEEE. 2017
View details for Web of Science ID 000424696901140
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SnapVX: A Network-Based Convex Optimization Solver.
Journal of machine learning research : JMLR
2017; 18 (1): 110–14
Abstract
SnapVX is a high-performance solver for convex optimization problems defined on networks. For problems of this form, SnapVX provides a fast and scalable solution with guaranteed global convergence. It combines the capabilities of two open source software packages: Snap.py and CVXPY. Snap.py is a large scale graph processing library, and CVXPY provides a general modeling framework for small-scale subproblems. SnapVX offers a customizable yet easy-to-use Python interface with "out-of-the-box" functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, parallelize, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website at http://snap.stanford.edu/snapvx.
View details for PubMedID 29599649
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Antagonistic control
SYSTEMS & CONTROL LETTERS
2016; 98: 44-48
View details for DOI 10.1016/j.sysconle.2016.10.002
View details for Web of Science ID 000390499500007
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Optimizing Stereotactic Radiosurgery Treatment of Multiple Brain Metastasis Lesions With Individualized Rotational Arc Trajectory
ELSEVIER SCIENCE INC. 2016: S228
View details for DOI 10.1016/j.ijrobp.2016.06.567
View details for Web of Science ID 000387655805155
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Optimization of rotational arc station parameter optimized radiation therapy.
Medical physics
2016; 43 (9): 4973-?
Abstract
To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites.The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU.Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6%, respectively. For the brain case, the doses to the eyes, chiasm, and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the head and neck case.The dosimetric quality and delivery efficiency presented here indicate that SPORT is an intriguing alternative treatment modality. With the widespread adoption of digital linac, SPORT should lead to improved patient care in the future.
View details for DOI 10.1118/1.4960000
View details for PubMedID 27587028
View details for PubMedCentralID PMC4975754
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Risk-Constrained Kelly Gambling
JOURNAL OF INVESTING
2016; 25 (3): 118-134
View details for DOI 10.3905/joi.2016.25.3.118
View details for Web of Science ID 000410586000013
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Variations and extension of the convex-concave procedure
OPTIMIZATION AND ENGINEERING
2016; 17 (2): 263-287
View details for DOI 10.1007/s11081-015-9294-x
View details for Web of Science ID 000378411000001
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Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
2016; 169 (3): 1042-1068
View details for DOI 10.1007/s10957-016-0892-3
View details for Web of Science ID 000376293800016
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Bounding duality gap for separable problems with linear constraints
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
2016; 64 (2): 355-378
View details for DOI 10.1007/s10589-015-9819-4
View details for Web of Science ID 000376872900002
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MIMO PID tuning via iterated LMI restriction
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
2016; 26 (8): 1718-1731
View details for DOI 10.1002/rnc.3376
View details for Web of Science ID 000374004500009
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CVXPY: A Python-Embedded Modeling Language for Convex Optimization.
Journal of machine learning research : JMLR
2016; 17
Abstract
CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.
View details for PubMedID 27375369
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A PRIMER ON MONOTONE OPERATOR METHODS SURVEY
APPLIED AND COMPUTATIONAL MATHEMATICS
2016; 15 (1): 3-43
View details for Web of Science ID 000371447300001
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Introduction
FOUNDATIONS AND TRENDS IN MACHINE LEARNING
2016; 9 (1): 2-+
View details for DOI 10.1561/2200000055
View details for Web of Science ID 000383972700001
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CVXPY: A Python-Embedded Modeling Language for Convex Optimization
JOURNAL OF MACHINE LEARNING RESEARCH
2016; 17
View details for Web of Science ID 000391526800001
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Maximum Torque-per-Current Control of Induction Motors via Semidefinite Programming
IEEE. 2016: 1920-1925
View details for Web of Science ID 000400048102017
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Line Search for Averaged Operator Iteration
IEEE. 2016: 1015-1022
View details for Web of Science ID 000400048101032
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Disciplined Convex-Concave Programming
IEEE. 2016: 1009-1014
View details for Web of Science ID 000400048101031
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A New Architecture for Optimization Modeling Frameworks
IEEE. 2016: 36-44
View details for DOI 10.1109/PyHPC.2016.5
View details for Web of Science ID 000401923200005
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Optimal Resource Allocation for Energy Efficient Transmission in DSL
IEEE. 2016
View details for Web of Science ID 000401963303124
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Matrix-Free Convex Optimization Modeling
OPTIMIZATION AND ITS APPLICATIONS IN CONTROL AND DATA SCIENCES: IN HONOR OF BORIS T. POLYAK'S 80TH BIRTHDAY
2016; 115: 221-264
View details for DOI 10.1007/978-3-319-42056-1_7
View details for Web of Science ID 000412415400008
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A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
JOURNAL OF MACHINE LEARNING RESEARCH
2016; 17
View details for Web of Science ID 000391664200001
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Metric selection in fast dual forward-backward splitting
AUTOMATICA
2015; 62: 1-10
View details for DOI 10.1016/j.automatica.2015.09.010
View details for Web of Science ID 000366233700001
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A perspective-based convex relaxation for switched-affine optimal control
SYSTEMS & CONTROL LETTERS
2015; 86: 34-40
View details for DOI 10.1016/j.sysconle.2015.09.002
View details for Web of Science ID 000366342100005
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Network Lasso: Clustering and Optimization in Large Graphs.
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
2015; 2015: 387-396
Abstract
Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of problems. Therefore, there is a need for simple, scalable algorithms that can solve many common optimization problems. In this paper, we introduce the network lasso, a generalization of the group lasso to a network setting that allows for simultaneous clustering and optimization on graphs. We develop an algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in a distributed and scalable manner, which allows for guaranteed global convergence even on large graphs. We also examine a non-convex extension of this approach. We then demonstrate that many types of problems can be expressed in our framework. We focus on three in particular - binary classification, predicting housing prices, and event detection in time series data - comparing the network lasso to baseline approaches and showing that it is both a fast and accurate method of solving large optimization problems.
View details for PubMedID 27398260
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Extensions of Gauss Quadrature Via Linear Programming
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
2015; 15 (4): 953-971
View details for DOI 10.1007/s10208-014-9197-9
View details for Web of Science ID 000358262500004
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Approximate dynamic programming via iterated Bellman inequalities
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
2015; 25 (10): 1472-1496
View details for DOI 10.1002/rnc.3152
View details for Web of Science ID 000354641300004
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Optimal current waveforms for brushless permanent magnet motors
INTERNATIONAL JOURNAL OF CONTROL
2015; 88 (7): 1389-1399
View details for DOI 10.1080/00207179.2015.1012558
View details for Web of Science ID 000355733700007
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Model predictive control for wind power gradients
WIND ENERGY
2015; 18 (6): 991-1006
View details for DOI 10.1002/we.1742
View details for Web of Science ID 000353355900003
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TH-AB-BRB-02: Enabling Web-Based Treatment Planning Using a State-Of-The-Art Convex Optimization Solver.
Medical physics
2015; 42 (6): 3704-?
Abstract
To develop an ultra-fast web-based inverse planning framework for VMAT/IMRT. To achieve high speed, we investigate the use of a simple convex formulation of the inverse treatment planning problem that takes advantage of recent developments in the field of distributed optimization.A Monte Carlo (MC) dose calculation algorithm was used to calculate the dose matrix (268228 voxels x 360 beams, 96M non-zeros) for a 360-aperture, 4-arc VMAT plan taken from the clinic. We wrote the objective for the inverse treatment planning problem as a sum of convex (piecewise-linear) penalties on the dose at each voxel in the planning volume. This convex voxel-separable formulation allowed us to apply a new, open-source, CPU- and GPU-capable optimization solver (http://foges.github.io/pogs/) to calculate our solutions of optimal beam intensities. In each planning session, after performing one full optimization we accelerated subsequent runs by "warm-starting": for run k, the optimal solution from run k-1 was used as an initial guess. We implemented the treatment planning application as a Python web server running on a standard g2-2xlarge GPU node on Amazon EC2.Our method formed optimal treatment plans in 5-15 seconds. Warm-start times ranged from 100ms-8s (mean 3s) while sweeping out a 5-log range of tradeoffs between target coverage and OAR sparing in 1000 total optimizations. Satisfactory plans were reached in 1-10 iterations of the optimization, with total planning time <10 minutes. Dosimetric characteristics such as the DVH curves showed that the resultant plans were comparable or superior to the clinically delivered plan.This work demonstrates the feasibility of high-quality, low-latency treatment planning using a convex problem formulation and GPU- based convex solver, making it practical to manipulate treatment objectives and view DVH curves and dose-wash views in nearly real-time in a web application. Funding support for this work is provided by the Stanford Bio-X Bowes Graduate Fellowship and NIH Grant 5R01CA176553.
View details for DOI 10.1118/1.4926133
View details for PubMedID 26129432
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Linear Models Based on Noisy Data and the Frisch Scheme.
SIAM review. Society for Industrial and Applied Mathematics
2015; 57 (2): 167-197
Abstract
We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is that measurement errors in each variable are independent. This basic formulation has its roots in the work of Charles Spearman in 1904 and of Ragnar Frisch in the 1930s. Various topics such as errors-in-variables, factor analysis, and instrumental variables all refer to alternative viewpoints on this problem and on ways to account for the anticipated way that noise enters the data. In the present paper we begin by describing certain fundamental contributions by the founders of the field and provide alternative modern proofs to certain key results. We then go on to consider a modern viewpoint and novel numerical techniques to the problem. The central theme is expressed by the Frisch-Kalman dictum, which calls for identifying a noise contribution that allows a maximal number of simultaneous linear relations among the noise-free variables-a rank minimization problem. In the years since Frisch's original formulation, there have been several insights, including trace minimization as a convenient heuristic to replace rank minimization. We discuss convex relaxations and theoretical bounds on the rank that, when met, provide guarantees for global optimality. A complementary point of view to this minimum-rank dictum is presented in which models are sought leading to a uniformly optimal quadratic estimation error for the error-free variables. Points of contact between these formalisms are discussed, and alternative regularization schemes are presented.
View details for DOI 10.1137/130921179
View details for PubMedID 27168672
View details for PubMedCentralID PMC4856315
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Linear Models Based on Noisy Data and the Frisch Scheme
SIAM REVIEW
2015; 57 (2): 167-197
Abstract
We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is that measurement errors in each variable are independent. This basic formulation has its roots in the work of Charles Spearman in 1904 and of Ragnar Frisch in the 1930s. Various topics such as errors-in-variables, factor analysis, and instrumental variables all refer to alternative viewpoints on this problem and on ways to account for the anticipated way that noise enters the data. In the present paper we begin by describing certain fundamental contributions by the founders of the field and provide alternative modern proofs to certain key results. We then go on to consider a modern viewpoint and novel numerical techniques to the problem. The central theme is expressed by the Frisch-Kalman dictum, which calls for identifying a noise contribution that allows a maximal number of simultaneous linear relations among the noise-free variables-a rank minimization problem. In the years since Frisch's original formulation, there have been several insights, including trace minimization as a convenient heuristic to replace rank minimization. We discuss convex relaxations and theoretical bounds on the rank that, when met, provide guarantees for global optimality. A complementary point of view to this minimum-rank dictum is presented in which models are sought leading to a uniformly optimal quadratic estimation error for the error-free variables. Points of contact between these formalisms are discussed, and alternative regularization schemes are presented.
View details for DOI 10.1137/130921179
View details for Web of Science ID 000354985600001
View details for PubMedCentralID PMC4856315
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Network Lasso: Clustering and Optimization in Large Graphs
ASSOC COMPUTING MACHINERY. 2015: 387-396
View details for DOI 10.1145/2783258.2783313
View details for Web of Science ID 000485312900043
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Non-Parametric Regression Modeling for Stochastic Optimization of Power Grid Load Forecast
IEEE. 2015: 1010-1015
View details for Web of Science ID 000370259201015
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Convex Optimization with Abstract Linear Operators
IEEE. 2015: 675-683
View details for DOI 10.1109/ICCV.2015.84
View details for Web of Science ID 000380414100076
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Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization
AUAI PRESS. 2015: 62-71
View details for Web of Science ID 000493121100007
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Optimizing Beam Angles and Aperture Shapes Simultaneously for Station Parameter Optimized Radiation Therapy (SPORT)
ELSEVIER SCIENCE INC. 2014: S108-S109
View details for DOI 10.1016/j.ijrobp.2014.05.526
View details for Web of Science ID 000342331400246
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Minimum-time speed optimisation over a fixed path
INTERNATIONAL JOURNAL OF CONTROL
2014; 87 (6): 1297-1311
View details for DOI 10.1080/00207179.2013.875224
View details for Web of Science ID 000333996000017
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Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)
WILEY. 2014
View details for DOI 10.1118/1.4888627
View details for Web of Science ID 000436933200003
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Optimal Crowd-Powered Rating and Filtering Algorithms
PROCEEDINGS OF THE VLDB ENDOWMENT
2014; 7 (9): 685-696
View details for DOI 10.14778/2732939.2732942
View details for Web of Science ID 000219810200004
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Block splitting for distributed optimization
MATHEMATICAL PROGRAMMING COMPUTATION
2014; 6 (1): 77-102
View details for DOI 10.1007/s12532-013-0061-8
View details for Web of Science ID 000219348600004
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Quadratic approximate dynamic programming for input-affine systems
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
2014; 24 (3): 432-449
View details for DOI 10.1002/rnc.2894
View details for Web of Science ID 000329439900003
- Proximal algorithms Foundations and Trends in Optimization 2014; 3 (1): 123-231
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Diagonal Scaling in Douglas-Rachford Splitting and ADMM
IEEE. 2014: 5033-5039
View details for Web of Science ID 000370073805033
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Preconditioning in Fast Dual Gradient Methods
IEEE. 2014: 5040-5045
View details for Web of Science ID 000370073805034
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Monotonicity and Restart in Fast Gradient Methods
IEEE. 2014: 5058-5063
View details for Web of Science ID 000370073805037
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Decentralized control of plug-in electric vehicles under driving uncertainty
IEEE. 2014
View details for Web of Science ID 000393467600259
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A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2014
View details for Web of Science ID 000452647100101
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Security Constrained Optimal Power Flow via Proximal Message Passing
Clemson-University Power Systems Conference (PSC)
IEEE. 2014
View details for Web of Science ID 000352000600040
- Performance bounds and suboptimal policies for multi-period investment Foundations and Trends in Optimization, Original version 2014; 1 (1): 1-69
- Dynamic network energy management via proximal message passing Foundations and Trends in Optimization, Original version posted 4/1/12. 2014; 2 (1): 70-122
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A Splitting Method for Optimal Control
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
2013; 21 (6): 2432-2442
View details for DOI 10.1109/TCST.2012.2231960
View details for Web of Science ID 000326105800040
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Nonconvex model predictive control for commercial refrigeration
INTERNATIONAL JOURNAL OF CONTROL
2013; 86 (8): 1349-1366
View details for DOI 10.1080/00207179.2012.742207
View details for Web of Science ID 000322807900004
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Risk group detection and survival function estimation for interval coded survival methods
NEUROCOMPUTING
2013; 112: 200-210
View details for DOI 10.1016/j.neucom.2012.12.049
View details for Web of Science ID 000319952500019
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A distributed algorithm for fitting generalized additive models
OPTIMIZATION AND ENGINEERING
2013; 14 (2): 213-224
View details for DOI 10.1007/s11081-013-9215-9
View details for Web of Science ID 000319884600001
- A primal-dual operator splitting method for conic optimization Working Draft. 2013
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Code Generation for Embedded Second-Order Cone Programming
European Control Conference (ECC)
IEEE. 2013: 1547–1552
View details for Web of Science ID 000332509701153
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Cost Optimal Operation of Thermal Energy Storage System with Real-Time Prices
2nd International Conference on Control, Automation and Information Sciences (ICCAIS)
IEEE. 2013
View details for Web of Science ID 000345828200041
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PID Design by Convex-Concave Optimization
European Control Conference (ECC)
IEEE. 2013: 4460–4465
View details for Web of Science ID 000332509704144
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ECOS: An SOCP Solver for Embedded Systems
European Control Conference (ECC)
IEEE. 2013: 3077–3082
View details for Web of Science ID 000332509703078
- ECOS: An SOCP solver for embedded systems 2013
- Cost optimal operation of thermal energy storage system with real-time prices 2013
- Code generation for embedded second-order cone programming 2013
- Iterated approximate value functions 2013
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MPC for Wind Power Gradients - Utilizing Forecasts, Rotor Inertia, and Central Energy Storage
European Control Conference (ECC)
IEEE. 2013: 4071–4076
View details for Web of Science ID 000332509704079
- Model predictive control for wind power gradients 2013
- Block splitting for distributed optimization Mathematical Programming Computation, Shorter preliminary version appeared as NIPS workshop paper. 2013
- PID design by convex-concave optimization 2013
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Robust optimization of adiabatic tapers for coupling to slow-light photonic-crystal waveguides
OPTICS EXPRESS
2012; 20 (19): 21558-21575
Abstract
We investigate the design of taper structures for coupling to slow-light modes of various photonic-crystal waveguides while taking into account parameter uncertainties inherent in practical fabrication. Our short-length (11 periods) robust tapers designed for ? = 1.55?m and a slow-light group velocity of c/34 have a total loss of < 20 dB even in the presence of nanometer-scale surface roughness, which outperform the corresponding non-robust designs by an order of magnitude. We discover a posteriori that the robust designs have smooth profiles that can be parameterized by a few-term (intrinsically smooth) sine series which helps the optimization to further boost the performance slightly. We ground these numerical results in an analytical foundation by deriving the scaling relationships between taper length, taper smoothness, and group velocity with the help of an exact equivalence with Fourier analysis.
View details for Web of Science ID 000308865600094
View details for PubMedID 23037275
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Dose optimization with first-order total-variation minimization for dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT)
MEDICAL PHYSICS
2012; 39 (7): 4316-4327
Abstract
A new treatment scheme coined as dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT) has recently been proposed to bridge the gap between IMRT and VMAT. By increasing the angular sampling of radiation beams while eliminating dispensable segments of the incident fields, DASSIM-RT is capable of providing improved conformity in dose distributions while maintaining high delivery efficiency. The fact that DASSIM-RT utilizes a large number of incident beams represents a major computational challenge for the clinical applications of this powerful treatment scheme. The purpose of this work is to provide a practical solution to the DASSIM-RT inverse planning problem.The inverse planning problem is formulated as a fluence-map optimization problem with total-variation (TV) minimization. A newly released L1-solver, template for first-order conic solver (TFOCS), was adopted in this work. TFOCS achieves faster convergence with less memory usage as compared with conventional quadratic programming (QP) for the TV form through the effective use of conic forms, dual-variable updates, and optimal first-order approaches. As such, it is tailored to specifically address the computational challenges of large-scale optimization in DASSIM-RT inverse planning. Two clinical cases (a prostate and a head and neck case) are used to evaluate the effectiveness and efficiency of the proposed planning technique. DASSIM-RT plans with 15 and 30 beams are compared with conventional IMRT plans with 7 beams in terms of plan quality and delivery efficiency, which are quantified by conformation number (CN), the total number of segments and modulation index, respectively. For optimization efficiency, the QP-based approach was compared with the proposed algorithm for the DASSIM-RT plans with 15 beams for both cases.Plan quality improves with an increasing number of incident beams, while the total number of segments is maintained to be about the same in both cases. For the prostate patient, the conformation number to the target was 0.7509, 0.7565, and 0.7611 with 80 segments for IMRT with 7 beams, and DASSIM-RT with 15 and 30 beams, respectively. For the head and neck (HN) patient with a complicated target shape, conformation numbers of the three treatment plans were 0.7554, 0.7758, and 0.7819 with 75 segments for all beam configurations. With respect to the dose sparing to the critical structures, the organs such as the femoral heads in the prostate case and the brainstem and spinal cord in the HN case were better protected with DASSIM-RT. For both cases, the delivery efficiency has been greatly improved as the beam angular sampling increases with the similar or better conformal dose distribution. Compared with conventional quadratic programming approaches, first-order TFOCS-based optimization achieves far faster convergence and smaller memory requirements in DASSIM-RT.The new optimization algorithm TFOCS provides a practical and timely solution to the DASSIM-RT or other inverse planning problem requiring large memory space. The new treatment scheme is shown to outperform conventional IMRT in terms of dose conformity to both the targetand the critical structures, while maintaining high delivery efficiency.
View details for DOI 10.1118/1.4729717
View details for Web of Science ID 000306893000029
View details for PubMedID 22830765
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Smoothed state estimates under abrupt changes using sum-of-norms regularization
AUTOMATICA
2012; 48 (4): 595-605
View details for DOI 10.1016/j.automatica.2011.08.063
View details for Web of Science ID 000302766400002
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A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology
PLOS ONE
2012; 7 (3)
Abstract
Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data.
View details for DOI 10.1371/journal.pone.0034312
View details for Web of Science ID 000304523400072
View details for PubMedID 22479598
View details for PubMedCentralID PMC3315538
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CVXGEN: a code generator for embedded convex optimization
OPTIMIZATION AND ENGINEERING
2012; 13 (1): 1-27
View details for DOI 10.1007/s11081-011-9176-9
View details for Web of Science ID 000299176900001
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Moving Horizon Estimation for Staged QP Problems
51st IEEE Annual Conference on Decision and Control (CDC)
IEEE. 2012: 3177–3182
View details for Web of Science ID 000327200403087
- Accuracy at the top 2012
- Quadratic approximate dynamic programming for input-affine systems International Journal of Robust and Nonlinear Control, published on-line 2012
- An ADMM algorithm for a class of total variation regularized estimation problems 2012
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Performance bounds and suboptimal policies for linear stochastic control via LMIs
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
2011; 21 (14): 1710-1728
View details for DOI 10.1002/rnc.1665
View details for Web of Science ID 000294256000007
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Fast Evaluation of Quadratic Control-Lyapunov Policy
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
2011; 19 (4): 939-946
View details for DOI 10.1109/TCST.2010.2056371
View details for Web of Science ID 000291808700021
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Receding Horizon Control AUTOMATIC GENERATION OF HIGH-SPEED SOLVERS
IEEE CONTROL SYSTEMS MAGAZINE
2011; 31 (3): 52-65
View details for DOI 10.1109/MCS.2011.940571
View details for Web of Science ID 000290630500009
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Inferring stable genetic networks from steady-state data
AUTOMATICA
2011; 47 (6): 1113-1122
View details for DOI 10.1016/j.automatica.2011.02.006
View details for Web of Science ID 000291456100004
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Inverse design of a three-dimensional nanophotonic resonator
OPTICS EXPRESS
2011; 19 (11): 10563-10570
Abstract
The inverse design of a three-dimensional nanophotonic resonator is presented. The design methodology is computationally fast (10 minutes on a standard desktop workstation) and utilizes a 2.5-dimensional approximation of the full three-dimensional structure. As an example, we employ the proposed method to design a resonator which exhibits a mode volume of 0.32(λ/n)3 and a quality factor of 7063.
View details for Web of Science ID 000290852800050
View details for PubMedID 21643310
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Self-Tuning for Maximized Lifetime Energy-Efficiency in the Presence of Circuit Aging
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
2011; 30 (5): 760-773
View details for DOI 10.1109/TCAD.2010.2100531
View details for Web of Science ID 000289843900010
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Controller coefficient truncation using Lyapunov performance certificate
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
2011; 21 (1): 55-78
View details for DOI 10.1002/rnc.1577
View details for Web of Science ID 000285936300004
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Imputing a Convex Objective Function
IEEE International Symposium on Intelligent Control (ISIC)/IEEE Multi-Conference on Systems and Control (MSC)
IEEE. 2011: 613–619
View details for Web of Science ID 000297500400015
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Load Reduction of Wind Turbines Using Receding Horizon Control
IEEE International Conference on Control Applications (CCA)
IEEE. 2011: 852–857
View details for Web of Science ID 000297216500091
- Min-max approximate dynamic programming 2011
- Scalable statistical monitoring of fleet data 2011
- Operation and configuration of a storage portfolio via convex optimization 2011
- Block splitting for large-scale distributed learning 2011
- Receding horizon control: Automatic generation of high-speed solvers IEEE Control Systems Magazine 2011; 3 (31): 52–65
- Distributed optimization and statistical learning via the alternating direction method of multipliers Foundations and Trends in Machine Learning 2011; 1 (3): 1–122
- Load reduction of wind turbines using receding horizon control 2011
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Wind Turbine Pitch Optimization
IEEE International Conference on Control Applications (CCA)
IEEE. 2011: 1327–1334
View details for Web of Science ID 000297216500151
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Shrinking-horizon dynamic programming
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
2010; 20 (17): 1993-2002
View details for DOI 10.1002/rnc.1566
View details for Web of Science ID 000284215900007
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A unified framework for 3D radiation therapy and IMRT planning: plan optimization in the beamlet domain by constraining or regularizing the fluence map variations
PHYSICS IN MEDICINE AND BIOLOGY
2010; 55 (22): N521-N531
Abstract
The purpose of this work is to demonstrate that physical constraints on fluence gradients in 3D radiation therapy (RT) planning can be incorporated into beamlet optimization explicitly by direct constraint on the spatial variation of the fluence maps or implicitly by using total-variation regularization (TVR). The former method forces the fluence to vary in accordance with the known form of a wedged field and latter encourages the fluence to take the known form of the wedged field by requiring the derivatives of the fluence maps to be piece-wise constant. The performances of the proposed methods are evaluated by using a brain cancer case and a head and neck case. It is found that both approaches are capable of providing clinically sensible 3D RT solutions with monotonically varying fluence maps. For currently available 3D RT delivery schemes based on the use of customized physical or dynamic wedges, constrained optimization seems to be more useful because the optimized fields are directly deliverable. Working in the beamlet domain provides a natural way to model the spatial variation of the beam fluence. The proposed methods take advantage of the fact that 3D RT is a special form of intensity-modulated radiation therapy (IMRT) and finds the optimal plan by searching for fields with a certain type of spatial variation. The approach provides a unified framework for 3D CRT and IMRT plan optimization.
View details for DOI 10.1088/0031-9155/55/22/N01
View details for Web of Science ID 000283789700001
View details for PubMedID 21030744
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Design of Affine Controllers via Convex Optimization
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2010; 55 (11): 2476-2487
View details for DOI 10.1109/TAC.2010.2046053
View details for Web of Science ID 000283940800003
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Compressed sensing based cone-beam computed tomography reconstruction with a first-order method
MEDICAL PHYSICS
2010; 37 (9): 5113-5125
Abstract
This article considers the problem of reconstructing cone-beam computed tomography (CBCT) images from a set of undersampled and potentially noisy projection measurements.The authors cast the reconstruction as a compressed sensing problem based on l1 norm minimization constrained by statistically weighted least-squares of CBCT projection data. For accurate modeling, the noise characteristics of the CBCT projection data are used to determine the relative importance of each projection measurement. To solve the compressed sensing problem, the authors employ a method minimizing total-variation norm, satisfying a prespecified level of measurement consistency using a first-order method developed by Nesterov.The method converges fast to the optimal solution without excessive memory requirement, thanks to the method of iterative forward and back-projections. The performance of the proposed algorithm is demonstrated through a series of digital and experimental phantom studies. It is found a that high quality CBCT image can be reconstructed from undersampled and potentially noisy projection data by using the proposed method. Both sparse sampling and decreasing x-ray tube current (i.e., noisy projection data) lead to the reduction of radiation dose in CBCT imaging.It is demonstrated that compressed sensing outperforms the traditional algorithm when dealing with sparse, and potentially noisy, CBCT projection views.
View details for DOI 10.1118/1.3481510
View details for Web of Science ID 000281906000063
View details for PubMedID 20964231
View details for PubMedCentralID PMC2945747
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Fast Algorithms for Resource Allocation in Wireless Cellular Networks
IEEE-ACM TRANSACTIONS ON NETWORKING
2010; 18 (3): 973-984
View details for DOI 10.1109/TNET.2009.2034850
View details for Web of Science ID 000278813900023
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Segmentation of ARX-models using sum-of-norms regularization
AUTOMATICA
2010; 46 (6): 1107-1111
View details for DOI 10.1016/j.automatica.2010.03.013
View details for Web of Science ID 000278675500020
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Techniques for exploring the suboptimal set
OPTIMIZATION AND ENGINEERING
2010; 11 (2): 319-337
View details for DOI 10.1007/s11081-009-9101-7
View details for Web of Science ID 000277790400006
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Real-Time Convex Optimization in Signal Processing
IEEE SIGNAL PROCESSING MAGAZINE
2010; 27 (3): 50-61
View details for DOI 10.1109/MSP.2010.936020
View details for Web of Science ID 000276819100008
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Mixed linear system estimation and identification
SIGNAL PROCESSING
2010; 90 (3): 966-971
View details for DOI 10.1016/j.sigpro.2009.09.022
View details for Web of Science ID 000272433400024
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Fast Model Predictive Control Using Online Optimization
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
2010; 18 (2): 267-278
View details for DOI 10.1109/TCST.2009.2017934
View details for Web of Science ID 000274996100003
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Compressed Sensing With Quantized Measurements
IEEE SIGNAL PROCESSING LETTERS
2010; 17 (2): 149-152
View details for DOI 10.1109/LSP.2009.2035667
View details for Web of Science ID 000272046700001
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Trajectory Generation Using Sum-of-Norms Regularization
49th IEEE Conference on Decision and Control (CDC)
IEEE. 2010: 540–545
View details for Web of Science ID 000295049100089
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Optimized Self-Tuning for Circuit Aging
IEEE. 2010: 586–91
View details for Web of Science ID 000397468600109
- Detecting aircraft performance anomalies from cruise flight data 2010
- Automatic code generation for real-time convex optimization Convex Optimization in Signal Processing and Communications edited by Eldar, Y., Palomar, D. Cambridge University Press. 2010: 1–41
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State Smoothing by Sum-of-Norms Regularization
49th IEEE Conference on Decision and Control (CDC)
IEEE. 2010: 2880–2885
View details for Web of Science ID 000295049103054
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Optimizing Adaptive Modulation in Wireless Networks via Multi-Period Network Utility Maximization
2010 IEEE International Conference on Communications
IEEE. 2010
View details for Web of Science ID 000290335602179
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Adaptive Modulation with Smoothed Flow Utility
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
2010
View details for DOI 10.1155/2010/815213
View details for Web of Science ID 000285616900001
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Adaptive Modulation in Wireless Networks with Smoothed Flow Utility
IEEE Global Telecommunications Conference (GLOBECOM 2010)
IEEE. 2010
View details for Web of Science ID 000287977405131
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Nonlinear Q-Design for Convex Stochastic Control
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2009; 54 (10): 2426-2430
View details for DOI 10.1109/TAC.2009.2029300
View details for Web of Science ID 000270950200015
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Processor Speed Control With Thermal Constraints
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
2009; 56 (9): 1994-2008
View details for DOI 10.1109/TCSI.2008.2011589
View details for Web of Science ID 000269684700001
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l(1) Trend Filtering
SIAM REVIEW
2009; 51 (2): 339-360
View details for DOI 10.1137/070690274
View details for Web of Science ID 000266289500003
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Relaxed maximum a posteriori fault identification
SIGNAL PROCESSING
2009; 89 (6): 989-999
View details for DOI 10.1016/j.sigpro.2008.11.014
View details for Web of Science ID 000264941900004
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Genetic network identification using convex programming
IET SYSTEMS BIOLOGY
2009; 3 (3): 155-166
Abstract
Gene regulatory networks capture interactions between genes and other cell substances, resulting in various models for the fundamental biological process of transcription and translation. The expression levels of the genes are typically measured as mRNA concentration in micro-array experiments. In a so-called genetic perturbation experiment, small perturbations are applied to equilibrium states and the resulting changes in expression activity are measured. One of the most important problems in systems biology is to use these data to identify the interaction pattern between genes in a regulatory network, especially in a large scale network. The authors develop a novel algorithm for identifying the smallest genetic network that explains genetic perturbation experimental data. By construction, our identification algorithm is able to incorporate and respect a priori knowledge known about the network structure. A priori biological knowledge is typically qualitative, encoding whether one gene affects another gene or not, or whether the effect is positive or negative. The method is based on a convex programming relaxation of the combinatorially hard problem of L(0) minimisation. The authors apply the proposed method to the identification of a subnetwork of the SOS pathway in Escherichia coli, the segmentation polarity network in Drosophila melanogaster, and an artificial network for measuring the performance of the method.
View details for DOI 10.1049/iet-syb.2008.0130
View details for Web of Science ID 000267060600003
View details for PubMedID 19449976
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Regular Analog/RF Integrated Circuits Design Using Optimization With Recourse Including Ellipsoidal Uncertainty
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
2009; 28 (5): 623-637
View details for DOI 10.1109/TCAD.2009.2013996
View details for Web of Science ID 000265486900002
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Performance bounds for linear stochastic control
SYSTEMS & CONTROL LETTERS
2009; 58 (3): 178-182
View details for DOI 10.1016/j.sysconle.2008.10.004
View details for Web of Science ID 000263494300003
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Optimal Estimation of Deterioration From Diagnostic Image Sequence
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2009; 57 (3): 1030-1043
View details for DOI 10.1109/TSP.2008.2009896
View details for Web of Science ID 000263431900018
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Analysis and Synthesis of State-Feedback Controllers With Timing Jitter
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2009; 54 (3): 652-657
View details for DOI 10.1109/TAC.2008.2010998
View details for Web of Science ID 000264397200027
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Convex piecewise-linear fitting
OPTIMIZATION AND ENGINEERING
2009; 10 (1): 1-17
View details for DOI 10.1007/s11081-008-9045-3
View details for Web of Science ID 000263388400001
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Sensor Selection via Convex Optimization
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2009; 57 (2): 451-462
View details for DOI 10.1109/TSP.2008.2007095
View details for Web of Science ID 000263161500004
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FASTEST MIXING MARKOV CHAIN ON GRAPHS WITH SYMMETRIES
SIAM JOURNAL ON OPTIMIZATION
2009; 20 (2): 792-819
View details for DOI 10.1137/070689413
View details for Web of Science ID 000268859300011
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Mixed Linear System Estimation and Identification
Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC)
IEEE. 2009: 1501–1506
View details for Web of Science ID 000336893601163
- Subspaces that minimize the condition number of a matrix Rejecta Mathematica 2009; 1 (1): 4-9
- Optimized self-tuning for circuit aging 2009
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Estimation of Faults in DC Electrical Power System
American Control Conference 2009
IEEE. 2009: 4334–4339
View details for Web of Science ID 000270044902059
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Distributed Large Scale Network Utility Maximization
IEEE International Symposium on Information Theory (ISIT 2009)
IEEE. 2009: 829–833
View details for Web of Science ID 000280141400169
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Cutting-set methods for robust convex optimization with pessimizing oracles
OPTIMIZATION METHODS & SOFTWARE
2009; 24 (3): 381-406
View details for DOI 10.1080/10556780802712889
View details for Web of Science ID 000265975500005
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An efficient method for large-scale slack allocation
ENGINEERING OPTIMIZATION
2009; 41 (12): 1163-1176
View details for DOI 10.1080/03052150902971682
View details for Web of Science ID 000208108300005
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Wireless NUM: Rate and Reliability Tradeoffs in Random Environments
IEEE Wireless Communications and Networking Conference
IEEE. 2009: 444–449
View details for Web of Science ID 000272057700080
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Robust design of slow-light tapers in periodic waveguides
ENGINEERING OPTIMIZATION
2009; 41 (4): 365-384
View details for DOI 10.1080/03052150802576797
View details for Web of Science ID 000264741600005
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Enhancing Sparsity by Reweighted l(1) Minimization
4th IEEE International Symposium on Biomedical Imaging
SPRINGER. 2008: 877–905
View details for DOI 10.1007/s00041-008-9045-x
View details for Web of Science ID 000261411300013
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An Efficient Method for Large-Scale Gate Sizing
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
2008; 55 (9): 2760-2773
View details for DOI 10.1109/TCSI.2008.920087
View details for Web of Science ID 000260863700030
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Filter design with low complexity coefficients
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2008; 56 (7): 3162-3169
View details for DOI 10.1109/TSP.2008.919386
View details for Web of Science ID 000257195900010
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Tractable approximate robust geometric programming
OPTIMIZATION AND ENGINEERING
2008; 9 (2): 95-118
View details for DOI 10.1007/s11081-007-9025-z
View details for Web of Science ID 000254251500001
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Compensation of multimode fiber dispersion using adaptive optics via convex optimization
JOURNAL OF LIGHTWAVE TECHNOLOGY
2008; 26 (9-12): 1295-1303
View details for DOI 10.1109/JLT.2008.917324
View details for Web of Science ID 000256971400030
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Robust beamforming via worst-case SINR maximization
IEEE TRANSACTIONS ON SIGNAL PROCESSING
2008; 56 (4): 1539-1547
View details for DOI 10.1109/TSP.2007.911498
View details for Web of Science ID 000254118300020
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Minimizing effective resistance of a graph
SIAM REVIEW
2008; 50 (1): 37-66
View details for DOI 10.1137/050645452
View details for Web of Science ID 000253646600004
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Design of low-bandwidth spatially distributed feedback
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2008; 53 (1): 257-272
View details for DOI 10.1109/TAC.2007.914950
View details for Web of Science ID 000254022000002
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FURTHER RELAXATIONS OF THE SEMIDEFINITE PROGRAMMING APPROACH TO SENSOR NETWORK LOCALIZATION
SIAM JOURNAL ON OPTIMIZATION
2008; 19 (2): 655-673
View details for DOI 10.1137/060669395
View details for Web of Science ID 000260849600008
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Graph Implementations for Nonsmooth Convex Programs
RECENT ADVANCES IN LEARNING AND CONTROL
2008; 371: 95-110
View details for DOI 10.1007/978-1-84800-155-8_7
View details for Web of Science ID 000267163500007
- Graph implementations for nonsmooth convex programs Recent Advances in Learning and Control (tribute to M. Vidyasagar), Lecture Notes in Control and Information Sciences edited by Boyd, S., Kimura, H. 2008: 95–110
- Two-fund separation under model mis-specification Working paper 2008
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Temperature control of high-performance multi-core platforms using convex optimization
Design, Automation and Test in Europe Conference and Exhibition (DATE 08)
IEEE. 2008: 108–113
View details for Web of Science ID 000257940700021
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Identification of stable genetic networks using convex programming
American Control Conference 2008
IEEE. 2008: 2755–2760
View details for Web of Science ID 000259261502020
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Learning the kernel via convex optimization
33rd IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE. 2008: 1997–2000
View details for Web of Science ID 000257456701161
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Mixed State Estimation for a Linear Gaussian Markov Model
47th IEEE Conference on Decision and Control
IEEE. 2008: 3219–3226
View details for Web of Science ID 000307311603058
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Cross-Layer Design with Adaptive Modulation: Delay, Rate, and Energy Tradeoffs
IEEE Global Telecommunications Conference (GLOBECOM 08)
IEEE. 2008
View details for DOI 10.1109/GLOCOM.2008.ECP.818
View details for Web of Science ID 000287978004070
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Optimizing adaptive modulation in wireless networks via utility maximization
IEEE International Conference on Communications (ICC 2008)
IEEE. 2008: 3372–3377
View details for Web of Science ID 000259505002124
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A MINIMAX THEOREM WITH APPLICATIONS TO MACHINE LEARNING, SIGNAL PROCESSING, AND FINANCE
SIAM JOURNAL ON OPTIMIZATION
2008; 19 (3): 1344-1367
View details for DOI 10.1137/060677586
View details for Web of Science ID 000263103900017
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Wireless Network Utility Maximization
IEEE Military Communications Conference (MILCOM 2008)
IEEE. 2008: 2314–2321
View details for Web of Science ID 000266709301072
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Mixed State Estimation for a Linear Gaussian Markov Model
10th International Conference on Control, Automation, Robotics and Vision
IEEE. 2008: 1005–1011
View details for Web of Science ID 000266716600175
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Fast computation of optimal contact forces
IEEE TRANSACTIONS ON ROBOTICS
2007; 23 (6): 1117-1132
View details for DOI 10.1109/TRO.2007.910774
View details for Web of Science ID 000251944100002
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A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing
OPTIMIZATION AND ENGINEERING
2007; 8 (4): 397-430
View details for DOI 10.1007/s11081-007-9011-5
View details for Web of Science ID 000249952000003
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An Interior-Point Method for Large-Scale l(1)-Regularized Least Squares
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
2007; 1 (4): 606-617
View details for DOI 10.1109/JSTSP.2007.910971
View details for Web of Science ID 000265494900008
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An interior-point method for large-scale l(1)-regularized logistic regression
JOURNAL OF MACHINE LEARNING RESEARCH
2007; 8: 1519-1555
View details for Web of Science ID 000249353700006
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Beamforming with uncertain weights
IEEE SIGNAL PROCESSING LETTERS
2007; 14 (5): 348-351
View details for DOI 10.1109/LSP.2006.888102
View details for Web of Science ID 000245910100014
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A tutorial on geometric programming
OPTIMIZATION AND ENGINEERING
2007; 8 (1): 67-127
View details for DOI 10.1007/s11081-007-9001-7
View details for Web of Science ID 000247679000004
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Generalized Chebyshev bounds via semidefinite programming
SIAM REVIEW
2007; 49 (1): 52-64
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Joint optimization of communication rates and linear systems
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Optimal allocation of local feedback in multistage amplifiers via geometric programming
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Optimizing dominant time constant in RC circuits
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View details for Web of Science ID A1992JE07600005
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EFFICIENT DISTANCE COMPUTATION USING BEST ELLIPSOID FIT
1992 IEEE INTERNATIONAL SYMP ON INTELLIGENT CONTROL
I E E E. 1992: 360–365
View details for Web of Science ID A1992BX94F00061
- Dynamics and control of a rapid thermal multiprocessor 1992
- On computing the worst-case peak gain of linear systems 1992
- Numerical methods for H_2 related problems 1992
- Multivariable feedback control of semiconductor wafer temperature 1992
- Computing bounds for the structured singular value via an interior point algorithm 1992
- Closed-loop convex analysis of performance limits for linear control systems 1992
- Branch-and-bound algorithm for computing the minimum stability degree of parameter-dependent linear systems International Journal of Robust and Nonlinear Control 1992; 4 (1): 295-317
- Interactive loop-shaping design of MIMO controllers 1992
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MODELING AND CONTROL OF RAPID THERMAL-PROCESSING
CONF ON RAPID THERMAL AND INTEGRATED PROCESSING
SPIE - INT SOC OPTICAL ENGINEERING. 1992: 2–17
View details for Web of Science ID A1992BV45H00001
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EXISTENCE AND UNIQUENESS OF OPTIMAL MATRIX SCALINGS
31ST IEEE CONF ON DECISION AND CONTROL
I E E E. 1992: 2010–2011
View details for Web of Science ID A1992BZ24X00458
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DESIGN OF STABILIZING STATE-FEEDBACK FOR DELAY SYSTEMS VIA CONVEX-OPTIMIZATION
31ST IEEE CONF ON DECISION AND CONTROL
I E E E. 1992: 147–148
View details for Web of Science ID A1992BZ24X00033
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ON COMPUTING THE WORST-CASE PEAK GAIN OF LINEAR-SYSTEMS
31ST IEEE CONF ON DECISION AND CONTROL
I E E E. 1992: 2191–2192
View details for Web of Science ID A1992BZ24X00497
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ON OPTIMAL SIGNAL SETS FOR DIGITAL-COMMUNICATIONS WITH FINITE PRECISION AND AMPLITUDE CONSTRAINTS
IEEE TRANSACTIONS ON COMMUNICATIONS
1991; 39 (2): 249-255
View details for Web of Science ID A1991FC43800016
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ROBUST-CONTROL DESIGN FOR ELLIPSOIDAL PLANT SET
30TH IEEE CONF ON DECISION AND CONTROL / 1991 ANNUAL MEETING OF THE IEEE CONTROL SYSTEM SOC
I E E E. 1991: 291–296
View details for Web of Science ID A1991BV05P00072
- Linear Controller Design – Limits of Performance Prentice-Hall. 1991
- Robust control design for ellipsoidal plant set 1991
- Computation of the maximum H_infinity-norm of parameter-dependent linear systems by a branch and bound algorithm 1991
- A robust control design for FIR plants with parameter set uncertainty 1991
- Linear Controller Design: Limits of Performance Prentice-Hall. 1991
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IMPROVEMENT OF TEMPERATURE UNIFORMITY IN RAPID THERMAL-PROCESSING SYSTEMS USING MULTIVARIABLE CONTROL
SYMP ON RAPID THERMAL AND INTEGRATED PROCESSING
MATERIALS RESEARCH SOC. 1991: 177–183
View details for Web of Science ID A1991BV07P00024
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COMPUTING THE MINIMUM STABILITY DEGREE OF PARAMETER-DEPENDENT LINEAR-SYSTEMS
INTERNATIONAL WORKSHOP ON ROBUST CONTROL
CRC PRESS INC. 1991: 359–378
View details for Web of Science ID A1991BX16Y00023
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COMPUTATION OF THE WORST-CASE COVARIANCE FOR LINEAR-SYSTEMS WITH UNCERTAIN PARAMETERS
30TH IEEE CONF ON DECISION AND CONTROL / 1991 ANNUAL MEETING OF THE IEEE CONTROL SYSTEM SOC
I E E E. 1991: 1941–1942
View details for Web of Science ID A1991BV05P00447
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A REGULARITY RESULT FOR THE SINGULAR-VALUES OF A TRANSFER-MATRIX AND A QUADRATICALLY CONVERGENT ALGORITHM FOR COMPUTING ITS L-INFINITY-NORM
SYSTEMS & CONTROL LETTERS
1990; 15 (1): 1-7
View details for Web of Science ID A1990DT26800001
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BOUNDS ON MAXIMUM THROUGHPUT FOR DIGITAL-COMMUNICATIONS WITH FINITE-PRECISION AND AMPLITUDE CONSTRAINTS
IEEE TRANSACTIONS ON INFORMATION THEORY
1990; 36 (3): 472-484
View details for Web of Science ID A1990DE94900002
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LINEAR CONTROLLER-DESIGN - LIMITS OF PERFORMANCE VIA CONVEX-OPTIMIZATION
PROCEEDINGS OF THE IEEE
1990; 78 (3): 529-574
View details for Web of Science ID A1990DD20300004
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PARAMETER SET ESTIMATION OF SYSTEMS WITH UNCERTAIN NONPARAMETRIC DYNAMICS AND DISTURBANCES
29th IEEE Conference on Decision and Control
I E E E. 1990: 3162–3167
View details for Web of Science ID A1990BA50L00684
- Identification of systems with parametric and nonparametric uncertainty 1990
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STABILITY ROBUSTNESS OF LINEAR-SYSTEMS TO REAL PARAMETRIC PERTURBATIONS
29th IEEE Conference on Decision and Control
I E E E. 1990: 1247–1248
View details for Web of Science ID A1990BA50L00273
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A BRANCH-AND-BOUND METHODOLOGY FOR MATRIX POLYTOPE STABILITY PROBLEMS ARISING IN POWER-SYSTEMS
29th IEEE Conference on Decision and Control
I E E E. 1990: 3022–3027
View details for Web of Science ID A1990BA50L00655
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STRUCTURED AND SIMULTANEOUS LYAPUNOV FUNCTIONS FOR SYSTEM STABILITY PROBLEMS
INTERNATIONAL JOURNAL OF CONTROL
1989; 49 (6): 2215-2240
View details for Web of Science ID A1989AK07000025
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STRUCTURED AND SIMULTANEOUS LYAPUNOV FUNCTIONS FOR SYSTEM STABILITY PROBLEMS
INTERNATIONAL WORKSHOP ON ROBUSTNESS IN IDENTIFICATION AND CONTROL : UNKNOWN BUT BOUNDED
PLENUM PRESS DIV PLENUM PUBLISHING CORP. 1989: 243–262
View details for Web of Science ID A1989BP72H00016
- Example of exact trade-offs in linear controller design 1989
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NUMERICAL-SOLUTION OF A 2-DISK PROBLEM
8TH ANNUAL AMERICAN CONTROL CONF ON AUTOMATION AND CONTROL
AMER AUTOMATIC CONTROL COUNCIL. 1989: 1745–1747
View details for Web of Science ID A1989BQ49K00315
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A REGULARITY RESULT FOR THE SINGULAR-VALUES OF A TRANSFER-MATRIX AND A QUADRATICALLY CONVERGENT ALGORITHM FOR COMPUTING ITS L-INFINITY-NORM
28TH CONF AT THE 1989 ANNUAL MEETING OF THE IEEE : DECISION AND CONTROL
I E E E. 1989: 954–955
View details for Web of Science ID A1989BP96Z00206
- A bisection method for computing the H_infinity-norm of a transfer matrix and related problems Mathematics of Control, Signals, and Systems 1989; 3 (2): 207-219
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EXACT TRADEOFFS IN LTI CONTROLLER-DESIGN - AN EXAMPLE
8TH ANNUAL AMERICAN CONTROL CONF ON AUTOMATION AND CONTROL
AMER AUTOMATIC CONTROL COUNCIL. 1989: 1274–1279
View details for Web of Science ID A1989BQ49K00227
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A NEW CAD METHOD AND ASSOCIATED ARCHITECTURES FOR LINEAR CONTROLLERS
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
1988; 33 (3): 268-283
View details for Web of Science ID A1988M275000006
- Perturbation bounds for structured robust stability 1988
- On computing the H_infinity-norm of a transfer matrix 1988
- On parametric H_infinity optimization 1988
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COMPARISON OF PEAK AND RMS GAINS FOR DISCRETE-TIME-SYSTEMS
SYSTEMS & CONTROL LETTERS
1987; 9 (1): 1-6
View details for Web of Science ID A1987J087800001
- On the spectral density of some stochastic processes Open Problems in Communication and Computation edited by Cover, T., Gopinath, B. Springer Verlag. 1987: 191–198
- Design of l1 optimal controllers 1987
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NECESSARY AND SUFFICIENT CONDITIONS FOR PARAMETER CONVERGENCE IN ADAPTIVE-CONTROL
AUTOMATICA
1986; 22 (6): 629-639
View details for Web of Science ID A1986F957600002
- A note on parametric and nonparametric uncertainties in control systems 1986
- A note on the order of l1-optimal compensators 1986
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FADING MEMORY AND THE PROBLEM OF APPROXIMATING NONLINEAR OPERATORS WITH VOLTERRA SERIES
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS
1985; 32 (11): 1150-1161
View details for Web of Science ID A1985ATH9700007
- Volterra series for nonlinear circuits 1985
- Low rate distributed quantization of noisy observations 1985
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UNIQUENESS OF CIRCUITS AND SYSTEMS CONTAINING ONE NONLINEARITY
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
1985; 30 (7): 674-680
View details for Web of Science ID A1985AJS1600008
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Subharmonic Functions and Performance Bounds on Linear Time-Invariant Feedback Systems
IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION
1985; 2 (2): 153-170
View details for DOI 10.1093/imamci/2.2.153
View details for Web of Science ID 000208453200006
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Analytical Foundations of Volterra Series
IMA JOURNAL OF MATHEMATICAL CONTROL AND INFORMATION
1984; 1 (3): 243-282
View details for DOI 10.1093/imamci/1.3.243
View details for Web of Science ID 000208452900003
- Structures for nonlinear systems 1984
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ON PARAMETER CONVERGENCE IN ADAPTIVE-CONTROL
SYSTEMS & CONTROL LETTERS
1983; 3 (6): 311-319
View details for Web of Science ID A1983RY93800001
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MEASURING VOLTERRA KERNELS
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS
1983; 30 (8): 571-577
View details for Web of Science ID A1983RD51900007
- Measuring Volterra kernels IEEE Transactions on Circuits and Systems 1983; 8 (30): 571-577
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UNIQUENESS OF A BASIC NON-LINEAR STRUCTURE
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS
1983; 30 (9): 648-651
View details for Web of Science ID A1983RK49700006
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ON THE PASSIVITY CRITERION FOR LTI N-PORTS
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
1982; 10 (4): 323-333
View details for Web of Science ID A1982PM01100003
- On optimal signal sets for digital communications with finite precision and amplitude constraints 1991, 1987