Bio


Yinyu Ye is currently the Kwoh-Ting Li Professor in the School of Engineering at the Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering, Stanford University. He received the B.S. degree in System Engineering from the Huazhong University of Science and Technology, China, and the M.S. and Ph.D. degrees in Engineering-Economic Systems and Operations Research from Stanford University. Ye's research interests lie in the areas of optimization, complexity theory, algorithm design and analysis, and applications of mathematical programming, operations research and system engineering. He is also interested in developing optimization software for various real-world applications. Current research topics include Liner Programming Algorithms, Markov Decision Processes, Computational Game/Market Equilibrium, Metric Distance Geometry, Dynamic Resource Allocation, and Stochastic and Robust Decision Making, etc. He is an INFORMS (The Institute for Operations Research and The Management Science) Fellow, and has received several research awards including the winner of the 2014 SIAG/Optimization Prize awarded every three years to the author(s) of the most outstanding paper, the inaugural 2012 ISMP Tseng Lectureship Prize for outstanding contribution to continuous optimization, the 2009 John von Neumann Theory Prize for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2006 Farkas prize on Optimization, and the 2009 IBM Faculty Award. He has supervised numerous doctoral students at Stanford who received received the 2015 and 2013 Second Prize of INFORMS Nicholson Student Paper Competition, the 2013 INFORMS Computing Society Prize, the 2008 Nicholson Prize, and the 2006 and 2010 INFORMS Optimization Prizes for Young Researchers. Ye teaches courses on Optimization, Network and Integer Programming, Semidefinite Programming, etc. He has written extensively on Interior-Point Methods, Approximation Algorithms, Conic Optimization, and their applications; and served as a consultant or technical board member to a variety of industries, including MOSEK.

Honors & Awards


  • SIAM Optimization Prize, SIAM (2015)
  • Lectureship Prize (Inaugural Recipient), ISMP Tseng (2012)
  • John von Neumann Theory Prize (Co-Recipient), INFORMS (2009)
  • Faculty of the Year Award, Stanford Asian American Society (2007)
  • Inaugural recipient of the Farkas Prize, INFORMS Optimization Society (2006)
  • Fellow, INFORMS (November 6, 2006)
  • Plenary speaker, International Symposium on Mathematical Programming, Berlin (2012)
  • Plenary speaker, Workshop on Internet and Network Economics (2008)
  • Semi-Plenary speaker, 17th International Symposium on Mathematical Programming, Atlanta (2000)
  • Area Editor, Optimization & Engineering (2000)
  • Associate Editor, Mathematics of Operations Research (1998-2001)
  • Section Officer (Linear Programming), Institute for Operations Research and the Management Sciences (1997-2000)
  • Co-organizer, DIMACS Princeton workshop on discrete optimizatio (1999)

Program Affiliations


  • Center for East Asian Studies

Professional Education


  • BS, Huazhong University of Science and Technology (HUST), China, Systems and Control (1982)
  • MS, Stanford University, Engineering Economic Systems (1983)
  • PhD, Stanford University, Engineering Economic Systems and Operations Research (1988)

Current Research and Scholarly Interests


My current research interests include Continuous and Discrete Optimization, Algorithm Development and Analyses, Algorithmic Game/Market Theory and Mechanism-Design, Markov Decision Process and Reinforcement Learning, Dynamic/Online Optimization and Resource Allocation, and Stochastic and Robust Decision Making. These areas have been the unique and core disciplines of MS&E, and extended to new application areas in AI, Machine Learning, Data Science, and Business Analytics.

2023-24 Courses


Stanford Advisees


All Publications


  • A gradient descent akin method for constrained optimization: algorithms and applications OPTIMIZATION METHODS & SOFTWARE Chen, L., Bletzinger, K., Gauger, N. R., Ye, Y. 2024
  • Worst-Case Iteration Bounds for Log Barrier Methods on Problems with Nonconvex Constraints MATHEMATICS OF OPERATIONS RESEARCH Hinder, O., Ye, Y. 2023
  • Optimization of Asset Allocation and Liquidation Time in Investment Decisions with VaR as a Risk Measure COMPUTATIONAL ECONOMICS Xu, C., Ye, Y. 2023
  • Fisher markets with linear constraints: Equilibrium properties and efficient distributed algorithms GAMES AND ECONOMIC BEHAVIOR Jalota, D., Pavone, M., Qi, Q., Ye, Y. 2023; 141: 223-260
  • Simple and fast algorithm for binary integer and online linear programming MATHEMATICAL PROGRAMMING Li, X., Sun, C., Ye, Y. 2022
  • An Improved Analysis of LP-Based Control for Revenue Management OPERATIONS RESEARCH Chen, G., Li, X., Ye, Y. 2022
  • Optimization and Operations Research in Mitigation of a Pandemic JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA Chen, C., Du, Y., Ge, D., Lei, L., Ye, Y. 2022; 10 (2): 289-304
  • High-Dimensional Learning Under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks OPERATIONS RESEARCH Liu, H., Ye, Y., Lee, H. 2022
  • Distributed Stochastic Optimization with Large Delays MATHEMATICS OF OPERATIONS RESEARCH Zhou, Z., Mertikopoulos, P., Bambos, N., Glynn, P., Ye, Y. 2021
  • Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds OPERATIONS RESEARCH Li, X., Ye, Y. 2021
  • Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs (vol 181, pg 1, 2020) MATHEMATICAL PROGRAMMING Burer, S., Ye, Y. 2021
  • Variance reduced value iteration and faster algorithms for solving Markov decision processes NAVAL RESEARCH LOGISTICS Sidford, A., Wang, M., Wu, X., Ye, Y. 2021

    View details for DOI 10.1002/nav.21992

    View details for Web of Science ID 000642314100001

  • The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks Li, X., Sun, C., Ye, Y., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • A Mathematical Programming Formulation for Optimal Load Shifting of Electricity Demand for the Smart Grid IEEE TRANSACTIONS ON BIG DATA Hu, R., Skorupski, R., Entriken, R., Ye, Y. 2020; 6 (4): 638–51
  • An ADMM-based interior-point method for large-scale linear programming OPTIMIZATION METHODS & SOFTWARE Lin, T., Ma, S., Ye, Y., Zhang, S. 2020
  • Managing randomization in the multi-block alternating direction method of multipliers for quadratic optimization MATHEMATICAL PROGRAMMING COMPUTATION Mihic, K., Zhu, M., Ye, Y. 2020
  • On the complexity of an expanded Tarski's fixed point problem under the componentwise ordering (vol 732, pg 26, 2018) THEORETICAL COMPUTER SCIENCE Dang, C., Ye, Y. 2020; 817: 80
  • Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs MATHEMATICAL PROGRAMMING Burer, S., Ye, Y. 2020; 181 (1): 1–17
  • On the Efficiency of Random Permutation for ADMM and Coordinate Descent MATHEMATICS OF OPERATIONS RESEARCH Sun, R., Luo, Z., Ye, Y. 2020; 45 (1): 233–71
  • MULTILEVEL MONTE CARLO SAMPLING ON HETEROGENEOUS COMPUTER ARCHITECTURES INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION Adcock, C., Ye, Y., Jofre, L., Laccarino, G. 2020; 10 (6): 575–94
  • Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity Sidford, A., Wang, M., Yang, L. F., Ye, Y., Chiappa, S., Calandra, R. ADDISON-WESLEY PUBL CO. 2020
  • On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods MATHEMATICAL PROGRAMMING Haeser, G., Hinder, O., Ye, Y. 2019
  • Towards solving 2-TBSG efficiently OPTIMIZATION METHODS & SOFTWARE Jia, Z., Wen, Z., Ye, Y. 2019
  • Optimality condition and complexity analysis for linearly-constrained optimization without differentiability on the boundary MATHEMATICAL PROGRAMMING Haeser, G., Liu, H., Ye, Y. 2019; 178 (1-2): 263–99
  • Sample Average Approximation with Sparsity-Inducing Penalty for High-Dimensional Stochastic Programming. Mathematical programming Liu, H., Wang, X., Yao, T., Li, R., Ye, Y. 2019; 78 (1-2): 69–108

    Abstract

    The theory on the traditional sample average approximation (SAA) scheme for stochastic programming (SP) dictates that the number of samples should be polynomial in the number of problem dimensions in order to ensure proper optimization accuracy. In this paper, we study a modification to the SAA in the scenario where the global minimizer is either sparse or can be approximated by a sparse solution. By making use of a regularization penalty referred to as the folded concave penalty (FCP), we show that, if an FCP-regularized SAA formulation is solved locally, then the required number of samples can be significantly reduced in approximating the global solution of a convex SP: the sample size is only required to be poly-logarithmic in the number of dimensions. The efficacy of the FCP regularizer for nonconvex SPs is also discussed. As an immediate implication of our result, a flexible class of folded concave penalized sparse M-estimators in high-dimensional statistical learning may yield a sound performance even when the problem dimension cannot be upper-bounded by any polynomial function of the sample size.

    View details for DOI 10.1007/s10107-018-1278-0

    View details for PubMedID 31680704

  • Worst-case complexity of cyclic coordinate descent: O(n(2)) gap with randomized version MATHEMATICAL PROGRAMMING Sun, R., Ye, Y. 2019
  • Approximation Hardness for A Class of Sparse Optimization Problems JOURNAL OF MACHINE LEARNING RESEARCH Chen, Y., Ye, Y., Wang, M. 2019; 20
  • Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem Ge, D., Wang, H., Xiong, Z., Ye, Y., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Extended ADMM and BCD for nonseparable convex minimization models with quadratic coupling terms: convergence analysis and insights MATHEMATICAL PROGRAMMING Chen, C., Li, M., Liu, X., Ye, Y. 2019; 173 (1-2): 37–77
  • On the complexity of an expanded Tarski's fixed point problem under the componentwise ordering THEORETICAL COMPUTER SCIENCE Dang, C., Ye, Y. 2018; 732: 26–45
  • A computation study on an integrated alternating direction method of multipliers for large scale optimization OPTIMIZATION LETTERS Zarepisheh, M., Xing, L., Ye, Y. 2018; 12 (1): 3–15
  • Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes Sidford, A., Wang, M., Wu, X., Ye, Y., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 770–87
  • Learning in Games with Lossy Feedback Zhou, Z., Mertikopoulos, P., Athey, S., Bambos, N., Glynn, P., Ye, Y., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model Sidford, A., Wang, M., Wu, X., Yang, L. F., Ye, Y., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • ON DOUBLY POSITIVE SEMIDEFINITE PROGRAMMING RELAXATIONS Fu, T., Ge, D., Ye, Y. GLOBAL SCIENCE PRESS. 2018: 391–403
  • Special issue on "Modern Optimization and Applications" Preface JOURNAL OF COMPUTATIONAL MATHEMATICS Chen, X., Dai, Y., Richtarik, P., Ye, Y. 2018; 36 (3): I-II
  • Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions. Mathematical programming Liu, H., Yao, T., Li, R., Ye, Y. 2017; 166 (1-2): 207-240

    Abstract

    This paper concerns the folded concave penalized sparse linear regression (FCPSLR), a class of popular sparse recovery methods. Although FCPSLR yields desirable recovery performance when solved globally, computing a global solution is NP-complete. Despite some existing statistical performance analyses on local minimizers or on specific FCPSLR-based learning algorithms, it still remains open questions whether local solutions that are known to admit fully polynomial-time approximation schemes (FPTAS) may already be sufficient to ensure the statistical performance, and whether that statistical performance can be non-contingent on the specific designs of computing procedures. To address the questions, this paper presents the following threefold results: (i) Any local solution (stationary point) is a sparse estimator, under some conditions on the parameters of the folded concave penalties. (ii) Perhaps more importantly, any local solution satisfying a significant subspace second-order necessary condition (S3ONC), which is weaker than the second-order KKT condition, yields a bounded error in approximating the true parameter with high probability. In addition, if the minimal signal strength is sufficient, the S3ONC solution likely recovers the oracle solution. This result also explicates that the goal of improving the statistical performance is consistent with the optimization criteria of minimizing the suboptimality gap in solving the non-convex programming formulation of FCPSLR. (iii) We apply (ii) to the special case of FCPSLR with minimax concave penalty (MCP) and show that under the restricted eigenvalue condition, any S3ONC solution with a better objective value than the Lasso solution entails the strong oracle property. In addition, such a solution generates a model error (ME) comparable to the optimal but exponential-time sparse estimator given a sufficient sample size, while the worst-case ME is comparable to the Lasso in general. Furthermore, to guarantee the S3ONC admits FPTAS.

    View details for DOI 10.1007/s10107-017-1114-y

    View details for PubMedID 29225375

    View details for PubMedCentralID PMC5720392

  • On a New SDP-SOCP Method for Acoustic Source Localization Problem ACM TRANSACTIONS ON SENSOR NETWORKS Gao, M., Yiu, K. C., Nordholm, S., Ye, Y. 2016; 12 (4)

    View details for DOI 10.1145/2968449

    View details for Web of Science ID 000388853800011

  • Hidden-City Ticketing: The Cause and Impact TRANSPORTATION SCIENCE Wang, Z., Ye, Y. 2016; 50 (1): 288-305
  • The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent MATHEMATICAL PROGRAMMING Chen, C., He, B., Ye, Y., Yuan, X. 2016; 155 (1-2): 57-79
  • The Simplex Method is Strongly Polynomial for Deterministic Markov Decision Processes MATHEMATICS OF OPERATIONS RESEARCH Post, I., Ye, Y. 2015; 40 (4): 859-868
  • A fixed point iterative approach to integer programming and its distributed computation FIXED POINT THEORY AND APPLICATIONS Dang, C., Ye, Y. 2015
  • A homogeneous interior-point algorithm for nonsymmetric convex conic optimization MATHEMATICAL PROGRAMMING Skajaa, A., Ye, Y. 2015; 150 (2): 391-422
  • Simultaneous beam sampling and aperture shape optimization for SPORT. Medical physics Zarepisheh, M., Li, R., Ye, Y., Xing, L. 2015; 42 (2): 1012-?

    Abstract

    Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes.The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution.A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case. It significantly improved the target conformality and at the same time critical structure sparing compared with conventional intensity modulated radiation therapy (IMRT). In the head and neck case, for example, the average PTV coverage D99% for two PTVs, cord and brainstem max doses, and right parotid gland mean dose were improved, respectively, by about 7%, 37%, 12%, and 16%.The proposed method automatically determines the number of the stations required to generate a satisfactory plan and optimizes simultaneously the involved station parameters, leading to improved quality of the resultant treatment plans as compared with the conventional IMRT plans.

    View details for DOI 10.1118/1.4906253

    View details for PubMedID 25652514

  • Complexity analysis of interior point algorithms for non-Lipschitz and nonconvex minimization MATHEMATICAL PROGRAMMING Bian, W., Chen, X., Ye, Y. 2015; 149 (1-2): 301-327
  • The Value of Stochastic Modeling in Two-Stage Stochastic Programs with Cost Uncertainty OPERATIONS RESEARCH Delage, E., Arroyo, S., Ye, Y. 2014; 62 (6): 1377-1393
  • Space tensor conic programming COMPUTATIONAL OPTIMIZATION AND APPLICATIONS Qi, L., Ye, Y. 2014; 59 (1-2): 307-319
  • A Levenberg-Marquardt method with approximate projections COMPUTATIONAL OPTIMIZATION AND APPLICATIONS Behling, R., Fischer, A., Herrich, M., Iusem, A., Ye, Y. 2014; 59 (1-2): 5-26
  • Waterflood management using two-stage optimization with streamline simulation COMPUTATIONAL GEOSCIENCES Wen, T., Thiele, M. R., Ciaurri, D. E., Aziz, K., Ye, Y. 2014; 18 (3-4): 483-504
  • A Dynamic Near-Optimal Algorithm for Online Linear Programming OPERATIONS RESEARCH Agrawal, S., Wang, Z., Ye, Y. 2014; 62 (4): 876-890
  • Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems OPERATIONS RESEARCH Wang, Z., Deng, S., Ye, Y. 2014; 62 (2): 318-331
  • Complexity of unconstrained minimization MATHEMATICAL PROGRAMMING Chen, X., Ge, D., Wang, Z., Ye, Y. 2014; 143 (1-2): 371-383
  • Analytical Results and Efficient Algorithm for Optimal Portfolio Deleveraging with Market Impact OPERATIONS RESEARCH Chen, J., Feng, L., Peng, J., Ye, Y. 2014; 62 (1): 195-206
  • A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles IEEE TRANSACTIONS ON SMART GRID Taheri, N., Entriken, R., Ye, Y. 2013; 4 (4): 1772-1779
  • Newsvendor optimization with limited distribution information OPTIMIZATION METHODS & SOFTWARE Zhu, Z., Zhang, J., Ye, Y. 2013; 28 (3): 640-667
  • On stress matrices of (d+1)-lateration frameworks in general position MATHEMATICAL PROGRAMMING Alfakih, A. Y., Taheri, N., Ye, Y. 2013; 137 (1-2): 1-17
  • On affine motions and bar frameworks in general position LINEAR ALGEBRA AND ITS APPLICATIONS Alfakih, A. Y., Ye, Y. 2013; 438 (1): 31-36
  • Beyond Convex Relaxation: A Polynomial-Time Non-Convex Optimization Approach to Network Localization 32nd IEEE INFOCOM Conference Ji, S., Sze, K., Zhou, Z., So, A. M., Ye, Y. IEEE. 2013: 2499–2507
  • THE CUBIC SPHERICAL OPTIMIZATION PROBLEMS MATHEMATICS OF COMPUTATION Zhang, X., Qi, L., Ye, Y. 2012; 81 (279): 1513-1525
  • A FPTAS for computing a symmetric Leontief competitive economy equilibrium MATHEMATICAL PROGRAMMING Zhu, Z., Dang, C., Ye, Y. 2012; 131 (1-2): 113-129
  • A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks JOURNAL OF THEORETICAL BIOLOGY Fleming, R. M., MAES, C. M., Saunders, M. A., Ye, Y., Palsson, B. O. 2012; 292: 71-77

    Abstract

    We derive a convex optimization problem on a steady-state nonequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the problem solution. This suggests a new variational principle for biochemical networks that can be implemented in a computationally tractable manner. We derive the Lagrange dual of the optimization problem and use strong duality to demonstrate that a biochemical analogue of Tellegen's theorem holds at optimality. Each optimal flux is dependent on a free parameter that we relate to an elementary kinetic parameter when mass action kinetics is assumed.

    View details for DOI 10.1016/j.jtbi.2011.09.029

    View details for Web of Science ID 000297450100008

    View details for PubMedID 21983269

  • Price of Correlations in Stochastic Optimization OPERATIONS RESEARCH Agrawal, S., Ding, Y., Saberi, A., Ye, Y. 2012; 60 (1): 150-162
  • Geometric rounding: a dependent randomized rounding scheme JOURNAL OF COMBINATORIAL OPTIMIZATION Ge, D., He, S., Ye, Y., Zhang, J. 2011; 22 (4): 699-725
  • The Simplex and Policy-Iteration Methods Are Strongly Polynomial for the Markov Decision Problem with a Fixed Discount Rate MATHEMATICS OF OPERATIONS RESEARCH Ye, Y. 2011; 36 (4): 593-603
  • An interior-point path-following algorithm for computing a Leontief economy equilibrium COMPUTATIONAL OPTIMIZATION AND APPLICATIONS Dang, C., Ye, Y., Zhu, Z. 2011; 50 (2): 223-236
  • A note on the complexity of L-p minimization MATHEMATICAL PROGRAMMING Ge, D., Jiang, X., Ye, Y. 2011; 129 (2): 285-299
  • An Optimization Approach to Improving Collections of Shape Maps COMPUTER GRAPHICS FORUM Andy Nguyen, A., Ben-Chen, M., Welnicka, K., Ye, Y., Guibas, L. 2011; 30 (5): 1481-1491
  • A Unified Framework for Dynamic Prediction Market Design OPERATIONS RESEARCH Agrawal, S., Delage, E., Peters, M., Wang, Z., Ye, Y. 2011; 59 (3): 550-568
  • Statistical ranking and combinatorial Hodge theory MATHEMATICAL PROGRAMMING Jiang, X., Lim, L., Yao, Y., Ye, Y. 2011; 127 (1): 203-244
  • Computing an Integer Point in a Class of Polytopes 10th International Symposium on Operations Research and Its Applications in Engineering, Technology and Management (ISO-RA) Dang, C., Ye, Y. WORLD PUBLISHING CORPORATION. 2011: 258–263
  • ON EQUILIBRIUM PRICING AS CONVEX OPTIMIZATION JOURNAL OF COMPUTATIONAL MATHEMATICS Chen, L., Ye, Y., Zhang, J. 2010; 28 (5): 569-578
  • Semidefinite Relaxation of Quadratic Optimization Problems IEEE SIGNAL PROCESSING MAGAZINE Luo, Z., Ma, W., So, A. M., Ye, Y., Zhang, S. 2010; 27 (3): 20-34
  • Special Issue in Memory of Alexander Rubinov PACIFIC JOURNAL OF OPTIMIZATION Fukushima, M., Kelley, C. T., Qi, L., Sun, J., Ye, Y. 2010; 6 (2)
  • Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems OPERATIONS RESEARCH Delage, E., Ye, Y. 2010; 58 (3): 595-612
  • Dynamic Spectrum Management With the Competitive Market Model IEEE TRANSACTIONS ON SIGNAL PROCESSING Xie, Y., Armbruster, B., Ye, Y. 2010; 58 (4): 2442-2446
  • A novel fluence map optimization model incorporating leaf sequencing constraints PHYSICS IN MEDICINE AND BIOLOGY Jin, R., Min, Z., Song, E., Liu, H., Ye, Y. 2010; 55 (4): 1243-1264

    Abstract

    A novel fluence map optimization model incorporating leaf sequencing constraints is proposed to overcome the drawbacks of the current objective inside smoothing models. Instead of adding a smoothing item to the objective function, we add the total number of monitor unit (TNMU) requirement directly to the constraints which serves as an important factor to balance the fluence map optimization and leaf sequencing optimization process at the same time. Consequently, we formulate the fluence map optimization models for the trailing (left) leaf synchronized, leading (right) leaf synchronized and the interleaf motion constrained non-synchronized leaf sweeping schemes, respectively. In those schemes, the leaves are all swept unidirectionally from left to right. Each of those models is turned into a linear constrained quadratic programming model which can be solved effectively by the interior point method. Those new models are evaluated with two publicly available clinical treatment datasets including a head-neck case and a prostate case. As shown by the empirical results, our models perform much better in comparison with two recently emerged smoothing models (the total variance smoothing model and the quadratic smoothing model). For all three leaf sweeping schemes, our objective dose deviation functions increase much slower than those in the above two smoothing models with respect to the decreasing of the TNMU. While keeping plans in the similar conformity level, our new models gain much better performance on reducing TNMU.

    View details for DOI 10.1088/0031-9155/55/4/023

    View details for Web of Science ID 000274206800023

    View details for PubMedID 20124655

  • UNIVERSAL RIGIDITY AND EDGE SPARSIFICATION FOR SENSOR NETWORK LOCALIZATION SIAM JOURNAL ON OPTIMIZATION Zhu, Z., So, A. M., Ye, Y. 2010; 20 (6): 3059-3081

    View details for DOI 10.1137/090772009

    View details for Web of Science ID 000285547100015

  • LOWER BOUND THEORY OF NONZERO ENTRIES IN SOLUTIONS OF l(2)-l(p) MINIMIZATION SIAM JOURNAL ON SCIENTIFIC COMPUTING Chen, X., Xu, F., Ye, Y. 2010; 32 (5): 2832-2852

    View details for DOI 10.1137/090761471

    View details for Web of Science ID 000283293500030

  • The Complexity of Determining the Uniqueness of Tarski's Fixed Point under the Lexicographic Ordering 6th International Workshop on Internet and Network Economics Dang, C., Ye, Y. SPRINGER-VERLAG BERLIN. 2010: 455–461
  • Universal Rigidity: Towards Accurate and Efficient Localization of Wireless Networks Conference on IEEE INFOCOM Zhu, Z., So, A. M., Ye, Y. IEEE. 2010
  • Correlation Robust Stochastic Optimization 21st Annual ACM/SIAM Symposium on Discrete Algorithms Agrawal, S., Ding, Y., Saberi, A., Ye, Y. SIAM. 2010: 1087–1096
  • Finding Equitable Convex Partitions of Points in a Polygon Efficiently ACM TRANSACTIONS ON ALGORITHMS Carlsson, J. G., Armbruster, B., Ye, Y. 2010; 6 (4)
  • Stochastic Combinatorial Optimization with Controllable Risk Aversion Level MATHEMATICS OF OPERATIONS RESEARCH So, A. M., Zhang, J., Ye, Y. 2009; 34 (3): 522-537
  • Conceptual formulation on four-dimensional inverse planning for intensity modulated radiation therapy PHYSICS IN MEDICINE AND BIOLOGY Lee, L., Ma, Y., Ye, Y., Xing, L. 2009; 54 (13): N255-N266

    Abstract

    Four-dimensional computed tomography (4DCT) offers an extra dimension of 'time' on the three-dimensional patient model with which we can incorporate target motion in radiation treatment (RT) planning and delivery in various ways such as in the concept of internal target volume, in gated treatment or in target tracking. However, for all these methodologies, different phases are essentially considered as non-interconnected independent phases for the purpose of optimization, in other words, the 'time' dimension has yet to be incorporated explicitly in the optimization algorithm and fully exploited. In this note, we have formulated a new 4D inverse planning technique that treats all the phases in the 4DCT as one single entity in the optimization. The optimization is formulated as a quadratic problem for disciplined convex programming that enables the problem to be analyzed and solved efficiently. In the proof-of-principle examples illustrated, we show that the temporal information of the spatial relation of the target and organs at risk could be 'exchanged' amongst different phases so that an appropriate weighting of dose deposition could be allocated to each phase, thus enabling a treatment with a tight target margin and a full duty cycle otherwise not achievable by either of the aforementioned methodologies. Yet there are practical issues to be solved in the 4D RT planning and delivery. The 4D concept in the optimization we have formulated here does provide insight on how the 'time' dimension can be exploited in the 4D optimization process.

    View details for DOI 10.1088/0031-9155/54/13/N01

    View details for Web of Science ID 000267137200025

    View details for PubMedID 19521008

  • An edge-reduction algorithm for the vertex cover problem OPERATIONS RESEARCH LETTERS Han, Q., Punnen, A. P., Ye, Y. 2009; 37 (3): 181-186
  • Solving Min-Max Multi-Depot Vehicle Routing Problem Workshop on Global Optimization - Methods and Applications Carlsson, J., Ge, D., Subramaniam, A., Ye, Y. AMER MATHEMATICAL SOC. 2009: 31–46
  • A Unified Framework for Dynamic Pari-Mutuel Information Market Design 10th ACM Conference on Electronic Commerce (EC-2009) Agrawal, S., Delage, E., Peters, M., Wang, Z., Ye, Y. ASSOC COMPUTING MACHINERY. 2009: 255–264
  • BIQUADRATIC OPTIMIZATION OVER UNIT SPHERES AND SEMIDEFINITE PROGRAMMING RELAXATIONS SIAM JOURNAL ON OPTIMIZATION Ling, C., Nie, J., Qi, L., Ye, Y. 2009; 20 (3): 1286-1310

    View details for DOI 10.1137/080729104

    View details for Web of Science ID 000277836500009

  • Budget Allocation in a Competitive Communication Spectrum Economy EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING Lin, M., Tsai, J., Ye, Y. 2009
  • Using total-variation regularization for intensity modulated radiation therapy inverse planning with field-specific numbers of segments PHYSICS IN MEDICINE AND BIOLOGY Zhu, L., Lee, L., Ma, Y., Ye, Y., Mazzeo, R., Xing, L. 2008; 53 (23): 6653-6672

    Abstract

    Currently, there are two types of treatment planning algorithms for intensity modulated radiation therapy (IMRT). The beamlet-based algorithm generates beamlet intensity maps with high complexity, resulting in large numbers of segments in the delivery after a leaf-sequencing algorithm is applied. The segment-based direct aperture optimization (DAO) algorithm includes the physical constraints of the deliverable apertures in the calculation, and achieves a conformal dose distribution using a small number of segments. However, the number of segments is pre-fixed in most of the DAO approaches, and the typical random search scheme in the optimization is computationally intensive. A regularization-based algorithm is proposed to overcome the drawbacks of the DAO method. Instead of smoothing the beamlet intensity maps as in many existing methods, we include a total-variation term in the optimization objective function to reduce the number of signal levels of the beam intensity maps. An aperture rectification algorithm is then applied to generate a significantly reduced number of deliverable apertures. As compared to the DAO algorithm, our method has an efficient form of quadratic optimization, with an additional advantage of optimizing field-specific numbers of segments based on the modulation complexity. The proposed approach is evaluated using two clinical cases. Under the condition that the clinical acceptance criteria of the treatment plan are satisfied, for the prostate patient, the total number of segments for five fields is reduced from 61 using the Eclipse planning system to 35 using the proposed algorithm; for the head and neck patient, the total number of segments for seven fields is reduced from 107 to 28. The head and neck result is also compared to that using an equal number of four segments for each field. The comparison shows that using field-specific numbers of segments achieves a much improved dose distribution.

    View details for DOI 10.1088/0031-9155/53/23/002

    View details for Web of Science ID 000260859000002

    View details for PubMedID 18997262

  • The complexity of equilibria: Hardness results for economies via a correspondence with games Workshop on Excursions in Algoritmics Codenotti, B., Saberi, A., Varadarajan, K., Ye, Y. ELSEVIER SCIENCE BV. 2008: 188–98
  • A Unified Theorem on SDP Rank Reduction MATHEMATICS OF OPERATIONS RESEARCH So, A. M., Ye, Y., Zhang, J. 2008; 33 (4): 910-920
  • Preface ALGORITHMICA Deng, X., Ye, Y. 2008; 52 (1): 1-2
  • FURTHER RELAXATIONS OF THE SEMIDEFINITE PROGRAMMING APPROACH TO SENSOR NETWORK LOCALIZATION SIAM JOURNAL ON OPTIMIZATION Wang, Z., Zheng, S., Ye, Y., Boyd, S. 2008; 19 (2): 655-673

    View details for DOI 10.1137/060669395

    View details for Web of Science ID 000260849600008

  • A FPTAS for Computing a Symmetric Leontief Competitive Economy Equilibrium 4th International Workshop on Internet and Network Economics Zhu, Z., Dang, C., Ye, Y. SPRINGER-VERLAG BERLIN. 2008: 31–40
  • Parimutuel Betting on Permutations 4th International Workshop on Internet and Network Economics Agrawal, S., Wang, Z., Ye, Y. SPRINGER-VERLAG BERLIN. 2008: 126–137
  • A distributed SDP approach for large-scale noisy anchor-free graph realization with applications to molecular conformation SIAM JOURNAL ON SCIENTIFIC COMPUTING Biswas, P., Toh, K., Ye, Y. 2008; 30 (3): 1251-1277

    View details for DOI 10.1137/05062754X

    View details for Web of Science ID 000255500500007

  • Algorithm 875: DSDP5 - Software for semidefinite programming ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE Benson, S. J., Ye, Y. 2008; 34 (3)
  • Fast aperture-based inverse treatment planning using mixed integer and quadratic optimization 50th Annual Meeting of the American-Society-for-Therapeutic-Radiology-and-Oncology (ASTRO) Zhu, L., Ma, Y., Lee, L., Ye, Y., Mazzeo, R., Xing, L. ELSEVIER SCIENCE INC. 2008: S675–S675
  • A path to the Arrow-Debreu competitive market equilibrium 5th Brazilian Workshop on Continuous Optimization Ye, Y. SPRINGER. 2008: 315–48
  • Exchange market equilibria with Leontief's utility: Freedom of pricing leads to rationality 1st International Workshop on Internet and Network Economics Ye, Y. ELSEVIER SCIENCE BV. 2007: 134–42
  • On approximating complex quadratic optimization problems via semidefinite programming relaxations 11th IPCO Conference So, A. M., Zhang, J., Ye, Y. SPRINGER. 2007: 93–110
  • A ubiquitin ligase transfers preformed polyubiquitin chains from a conjugating enzyme to a substrate NATURE Li, W., Tu, D., Brunger, A. T., Ye, Y. 2007; 446 (7133): 333-337

    Abstract

    In eukaryotic cells, many short-lived proteins are conjugated with Lys 48-linked ubiquitin chains and degraded by the proteasome. Ubiquitination requires an activating enzyme (E1), a conjugating enzyme (E2) and a ligase (E3). Most ubiquitin ligases use either a HECT (homologous to E6-associated protein C terminus) or a RING (really interesting new gene) domain to catalyse polyubiquitination, but the mechanism of E3 catalysis is poorly defined. Here we dissect this process using mouse Ube2g2 (E2; identical at the amino acid level to human Ube2g2) and human gp78 (E3), an endoplasmic reticulum (ER)-associated conjugating system essential for the degradation of misfolded ER proteins. We demonstrate by expressing recombinant proteins in Escherichia coli that Ube2g2/gp78-mediated polyubiquitination involves preassembly of Lys 48-linked ubiquitin chains at the catalytic cysteine of Ube2g2. The growth of Ube2g2-anchored ubiquitin chains seems to be mediated by an aminolysis-based transfer reaction between two Ube2g2 molecules that each carries a ubiquitin moiety in its active site. Intriguingly, polyubiquitination of a substrate can be achieved by transferring preassembled ubiquitin chains from Ube2g2 to a lysine residue in a substrate.

    View details for DOI 10.1038/nature05542

    View details for Web of Science ID 000244892900049

    View details for PubMedID 17310145

  • Theory of semidefinite programming for sensor network localization 16th Annual ACM-SIAM Symposium on Discrete Algorithms So, A. M., Ye, Y. SPRINGER. 2007: 367–84
  • Pari-mutuel markets: Mechanisms and performance 3rd International Workshop on Internet and Network Economics Peters, M., So, A. M., Ye, Y. SPRINGER-VERLAG BERLIN. 2007: 82–95
  • Approximating the radii of point sets SIAM JOURNAL ON COMPUTING Varadarajan, K., Venkatesh, S., Ye, Y., Zhang, J. 2007; 36 (6): 1764-1776

    View details for DOI 10.1137/050627472

    View details for Web of Science ID 000246299400012

  • Semidefinite programming approaches for sensor network localization with noisy distance measurements IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING Biswas, P., Liang, T., Toh, K., Ye, Y., Wang, T. 2006; 3 (4): 360-371
  • Improved complexity results on solving real-number linear feasibility problems MATHEMATICAL PROGRAMMING Ye, Y. Y. 2006; 106 (2): 339-363
  • Lot-sizing scheduling with batch setup times JOURNAL OF SCHEDULING Chen, B., Ye, Y. Y., Zhang, J. W. 2006; 9 (3): 299-310
  • Semidefinite Programming Based Algorithms for Sensor Network Localization ACM TRANSACTIONS ON SENSOR NETWORKS Biswas, P., Lian, T., Wang, T., Ye, Y. 2006; 2 (2)
  • Stochastic combinatorial optimization with controllable risk aversion level - (Extended abstract) 9th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems/10th International Workshop on Randomization and Computation So, A. M., Zhang, J., Ye, Y. SPRINGER-VERLAG BERLIN. 2006: 224–235
  • A Semidefinite Programming Approach to Tensegrity Theory and Realizability of Graphs 17th ACM-SIAM Symposium on Discrete Algorithms So, A. M., Ye, Y. SIAM. 2006: 766–775
  • Distributed method for solving semidefinite programs arising from ad hoc wireless sensor network localization Conference on Multiscale Optimization Methods and Applications Biswas, P., Ye, Y. Y. SPRINGER. 2006: 69–84
  • Area Editors' Statements OPERATIONS RESEARCH Fourer, R., Hazen, G. B., Oren, S. S., Broadie, M. N., Duenyas, I., Song, J. S., Simester, D., Kress, M., Ye, Y. Y., Trick, M. A., Graves, S. C., Zenios, S. A., van Ryzin, G. J., Henderson, S. G., Kumar, S., Balakrishnan, A., Ball, M. O. 2006; 54 (1): 5-10
  • SpaseLoc: An adaptive subproblem algorithm for scalable wireless sensor network localization SIAM JOURNAL ON OPTIMIZATION Carter, M. W., Jin, H. H., Saunders, M. A., Ye, Y. 2006; 17 (4): 1102-1128

    View details for DOI 10.1137/040621600

    View details for Web of Science ID 000244631800007

  • Approximation algorithms for metric facility location problems SIAM JOURNAL ON COMPUTING Mahdian, M., Ye, Y., Zhang, J. 2006; 36 (2): 411-432
  • Leontief Economies Encode Nonzero Sum Two-Player Games 17th ACM-SIAM Symposium on Discrete Algorithms Codenotti, B., Saberi, A., Varadarajan, K., Ye, Y. SIAM. 2006: 659–667
  • A new complexity result on solving the Markov decision problem MATHEMATICS OF OPERATIONS RESEARCH Ye, Y. Y. 2005; 30 (3): 733-749
  • A multiexchange local search algorithm for the capacitated facility location problem MATHEMATICS OF OPERATIONS RESEARCH Zhang, J. W., Chen, B., Ye, Y. Y. 2005; 30 (2): 389-403
  • On solving univariate sparse polynomials in logarithmic time Conference on Foundations of Computational Mathematics (FoCM) Rojas, J. M., Ye, Y. Y. ACADEMIC PRESS INC ELSEVIER SCIENCE. 2005: 87–110
  • On solving coverage problems in a wireless sensor network using Voronoi diagrams 1st International Workshop on Internet and Network Economics So, A. M., Ye, Y. Y. SPRINGER-VERLAG BERLIN. 2005: 584–593
  • Computing the Arrow-Debreu competitive market equilibrium and its extensions 1st International Conference on Algorithmic Applications in Management Ye, Y. Y. SPRINGER-VERLAG BERLIN. 2005: 3–5
  • Semidefinite programming algorithms for sensor network localization using angle information 39th Asilomar Conference on Signals, Systems and Computers Biswas, P., Aghajan, H., Ye, Y. IEEE. 2005: 220–224
  • On approximating complex quadratic optimization problems via semidefinite programming relaxations 11th International Integer Programming and Combinatorial Optimization Conference So, A. M., Zhang, J. W., Ye, Y. Y. SPRINGER-VERLAG BERLIN. 2005: 125–135
  • Theory of Semidefinite Programming for Sensor Network Localization 16th Annual ACM-SIAM Symposium on Discrete Algorithms So, A. M., Ye, Y. SIAM. 2005: 405–414
  • Exchange market equilibria with Leontief's utility: Freedom of pricing leads to rationality 1st International Workshop on Internet and Network Economics Ye, Y. Y. SPRINGER-VERLAG BERLIN. 2005: 14–23
  • Improved approximations for max set splitting and max NAE SAT DISCRETE APPLIED MATHEMATICS Zhang, J. W., Ye, Y. Y., Han, Q. M. 2004; 142 (1-3): 133-149
  • A multi-exchange local search algorithm for the capacitated facility location problem - (Extended abstract) 10th International Integer Programming and Combinatorial Optimization Conference Zhang, J. W., Chen, B., Ye, Y. Y. SPRINGER-VERLAG BERLIN. 2004: 219–233
  • Improved combinatorial approximation algorithms for the k-level facility location problem SIAM JOURNAL ON DISCRETE MATHEMATICS Ageev, A., Ye, Y. Y., Zhang, J. W. 2004; 18 (1): 207-217
  • Semidefinite programming for ad hoc wireless sensor network localization 3rd International Symposium on Information Processing in Sensor Networks Biswas, P., Ye, Y. Y. ASSOC COMPUTING MACHINERY. 2004: 46–54
  • Approximating the 2-catalog segmentation problem using semidefinite programming relaxations 1st International Conference on Optimization Methods and Software (OMS2002) Xu, D. C., Ye, Y. Y., Zhang, J. W. TAYLOR & FRANCIS LTD. 2003: 705–19
  • An approximation algorithm for scheduling two parallel machines with capacity constraints DISCRETE APPLIED MATHEMATICS Yang, H., Ye, Y. Y., Zhang, J. W. 2003; 130 (3): 449-467
  • An improved algorithm for approximating the radii of point sets 6th Int Workshop on Approximation Algorithms for Combinatorial Optimization Problems/7th Int Workshop on Randomization and Approximation Techniques in Computer Science Ye, Y. Y., Zhang, J. W. SPRINGER-VERLAG BERLIN. 2003: 178–187
  • Improved combinatorial approximation algorithms for the k-level facility location problem 30th International Colloquium on Automata, Languages and Programming (ICALP 2003) Ageev, A., Ye, Y. Y., Zhang, J. W. SPRINGER-VERLAG BERLIN. 2003: 145–156
  • New results on quadratic minimization SIAM JOURNAL ON OPTIMIZATION Ye, Y. Y., Zhang, S. Z. 2003; 14 (1): 245-267
  • A 2-approximation algorithm for the soft-capacitated facility location problem 6th Int Workshop on Approximation Algorithms for Combinatorial Optimization Problems/7th Int Workshop on Randomization and Approximation Techniques in Computer Science Mahdian, M., Ye, Y. Y., Zhang, J. W. SPRINGER-VERLAG BERLIN. 2003: 129–140
  • A note on the maximization version of the multi-level facility location problem OPERATIONS RESEARCH LETTERS Zhang, J. W., Ye, Y. Y. 2002; 30 (5): 333-335
  • An improved rounding method and semidefinite programming relaxation for graph partition 17th International Symposium of the Mathematical-Programming-Society Han, Q. M., Ye, Y. Y., Zhang, J. W. SPRINGER. 2002: 509–35
  • Improved approximation algorithms for metric facility location problems 5th International Workshop on Approximation Algorithms for Combinatorial Optimization Mahdian, M., Ye, Y. Y., Zhang, J. W. SPRINGER-VERLAG BERLIN. 2002: 229–242
  • Approximating maximum stable set and minimum graph coloring problems with the positive semidefinite relaxation International Conference on Complementarity 99 (ICCP99) Benson, S. J., Ye, Y. SPRINGER. 2001: 1–17
  • Blind Channel Equalization and Approximation Algorithms IEEE Trans. on Signal Processing Li, B. 2001; 49 (11): 2823-2831
  • A .699-approximation algorithm for Max-Bisection Mathematical Programming Ye, Y. 2001; 90: 101-111
  • On smoothing methods for the P0 matrix linear complementarity problem SIAM J. Optimization Chen, X., Ye, Y. 2001; 11: 341-363
  • An efficient algorithm for minimizing a sum of P-norms SIAM J. Optimization Xue, G., Ye, Y. 2000; 10: 551-579
  • A simplification to ``A Primal-Dual Interior Point Method Whose Running Time Depends Only on the Constraint Matrix'' High Performance Optimization, Applied Optimization 33 Vavasis, S., Ye, Y. edited by Zhang, et al, S. 2000: 233–243
  • Application of Semidefinite Programming to Circuit Partitioning Approximation and Complexity in Numerical Optimization Choi, C., Ye, Y. edited by Pardalos, P. Kluwer Academics Publishers. 2000: 130–136
  • Convergence results of analytic center estimator Analytic center approach to bounded error parameter IEEE Transactions on Automatic Control Bai, E., Fu, M., Tempo, R., Ye, Y. 2000
  • Solving large-scale sparse semidefinite programs for combinatorial optimization SIAM J. Optimization Benson, S., Ye, Y., Zhang, X. 2000; 10: 443-461
  • Probabilistic Analysis of an Infeasible Primal--Dual Algorithm for Linear Programming Mathematics of Operations Research Anstreicher, K., Ji, J., Potra, F., Ye, Y. 1999; 24: 176-192
  • On a homogeneous algorithm for the monotone complementarity problem Mathematical Programming Andersen, E., Ye, Y. 1999; 84: 375-400
  • Bounded Error Parameter Estimation: A Sequential Analytic Center Approach IEEE Transactions on Automatic Control Bai, E., Ye, Y., Tempo, R. 1999; 6 (44): 1107-1117
  • On Homotopy-Smoothing Methods for Variational Inequalities SIAM J. Control & Optimization Chen, X., Ye, Y. 1999; 37: 589-616
  • Infeasible-start primal-dual methods and infeasibility detectors for nonlinear programming problems Mathematical Programming Nesterov, Yu., Todd, M., J., Ye, Y. 1999; 84: 227-267
  • Constrained Logarithmic Least Squares in Parameter Estimation IEEE Transactions on Automatic Control Bai, E., Ye, Y. 1999; 1 (44): 182-185
  • A computational study of the homogeneous algorithm for large-scale convex optimization Computational Optimization and Applications Andersen, E., Ye, Y. 1998; 10: 243-269
  • Semidefinite Relaxations, Multivariate Normal Distributions, and Order Statistics Handbook of Combinatorial Optimization Bertsimas, D., Ye, Y. edited by Du, D., Z., Pardalos, P.M. Kluwer Academic Publishers. 1998: 1–19
  • On the complexity of approximating a KKT point of quadratic programming Mathematical Programming Ye, Y. 1998; 80: 195-212
  • Approximation algorithms for quadratic programming Journal of Combinatorial Optimization Fu, M., Luo, Z., Q., Ye, Y. 1998; 1 (2): 29-50
  • Approximate Farkas lemmas and stopping rules for iterative infeasible-point algorithms for linear programming Mathematical Programming Todd, M., J., Ye, Y. 1998; 81: 1-22
  • An infeasible interior-point algorithm for solving primal and dual geometric programs Mathematical Programming Kortanek, K., O., Xu, X., Ye, Y. 1997; 76: 155-182
  • On a homogeneous algorithm for a monotone complementarity problem with nonlinear equality constraints Complementarity and variational Problems: State of the art Andersen, E., Ye, Y. edited by Ferris, Michael, C., Pang, J. SIAM. 1997: 1–11
  • Improved complexity using higher-order correctors for primal-dual Dikin affine scaling Mathematical Programming Jansen, B., Roos, C., Terlaky, T., Ye, Y. 1997; 76: 117-130
  • Complexity analysis of the analytic center cutting plane method that uses multiple cuts Mathematical Programming Ye, Y. 1997; 78: 85-104
  • On homogeneous and self-dual algorithm for LCP Mathematical Programming Ye, Y. 1997; 76: 211-222
  • Efficient algorithms for minimizing a sum of Euclidean norms with applications SIAM J. Optimization Xue, G., Ye, Y. 1997; 7: 1017-1036
  • A primal-dual interior-point method whose running time depends only on the constraint matrix Mathematical Programming Vavasis, S., Ye, Y. 1996; 74: 79-120
  • How partial knowledge helps to solve linear programs Journal of Complexity Ye, Y. 1996; 12: 480-491
  • Complexity analysis of an interior-point cutting plane method for convex feasibility problem SIAM J. Optimization Goffin, J., Luo, Z., Ye, Y. 1996; 6: 638-652
  • Combining interior-point and pivoting algorithms for linear programming Management Science Andersen, E., D., Ye, Y. 1996; 42: 1719-1731
  • A lower bound on the number of iterations of long-step and polynomial interior-point linear programming algorithms Annals of Operations Research Todd, M., Ye, Y. 1996; 62: 233-252
  • A asymptotical O(√nL) -iteration path-following linear programming algorithm that uses long steps SIAM J. Optimization Ye, Y. 1996; 6: 570-586
  • Interior-point methods for nonlinear complementarity problem Journal of Optimization Theory and Application Potra, F., Ye, Y. 1996; 68
  • Identifying an optimal basis in linear programming Annals of Operations Research Vavasis, S., Ye, Y. 1996; 62: 565-572
  • A simplified homogeneous and self-dual linear programming algorithm and its implementation Annals of Operations Research Xu, X., Hung, P., Ye, Y. 1996; 62: 151-172
  • A surface of analytic centers and infeasible-interior-point algorithms for linear programming Mathematics of Operations Research Mizuno, S., Todd, M., Ye, Y. 1995; 20: 135-162
  • On the von Neumann economic growth problem Mathematics Operations Research Ye, Y. 1995; 20: 617-633
  • Condition numbers for polyhedra with real number data Operations Research Letters Vavasis, S., Ye, Y. 1995; 17: 209-214
  • A generalized homogeneous and self-dual linear programming algorithm Operations Research Letters Xu, X., Ye, Y. 1995; 17
  • On the convergence of the iteration sequence in primal-dual interior-point methods Mathematical Programming Tapia, R., Zhang, Y., Ye, Y. 1995; 68: 141-154
  • The optimal choice of inputs under time of use pricing, fixed proportions technology and adjustment costs: an application to industrial firms Management Sciences Spector, Y., Tishler, A., Ye, Y. 1995; 41: 1679-1692
  • A convergent algorithm for quantile regression with smoothing splines Computational Statistics & Data Analysis Bosch, R., J., Ye, Y., Woodworth, G., G. 1995; 19: 613-630
  • Specially structured uncapacitated facility location problems Operations Research Jones, P., Lowe, T., Muller, G., Xu, N., Ye, Y., Zydiak, J. 1995; 43: 661-669
  • Combining binary search and Newton's method to compute real roots for a class of real functions Journal of Complexity Ye, Y. 1994; 10: 271-280
  • ON THE COMPLEXITY OF A COLUMN GENERATION ALGORITHM FOR CONVEX OR QUASI-CONVEX FEASIBILITY PROBLEMS Conference on Large Scale Optimization Goffin, J. L., Luo, Z. Q., Ye, Y. Y. KLUWER ACADEMIC PUBL. 1994: 182–191
  • A genuine quadratically convergent polynomial interior point algorithm for linear programming Advances in Optimization and Approximation Ye, Y. edited by Du, D., Sun, J. Kluwer Academic Publishers, Boston. 1994: 1
  • On the complexity of a column generation algorithm for convex or quasiconvex feasibility problems Large Scale Optimization: State of the Art Goffin, J., Luo, Z., Ye, Y. edited by Hager, W., Hearn, D., Pardalos, P. Kluwer Academic Publishers, Boston. 1994: 182–191
  • A complexity analysis for interior-point algorithms based on Karmarkar's potential functions SIAM J. on Optimization Ji, J., Ye, Y. 1994; 4: 512-520
  • Toward probabilistic analysis of interior-point algorithms for linear programming Mathematics of Operations Research Ye, Y. 1994; 19: 38-52
  • An $O(\sqrt{n}L)$-iteration homogeneous and self-dual linear programming algorithm Mathematics of Operations Research Ye, Y., Todd, M., Mizuno, S. 1994; 19: 53-67
  • An accelerated interior-point method whose running time depends only on $A$ Vavasis, S., Ye, Y. 1994
  • A decomposition variant of the potential reduction algorithm for linear programming Management Science Kaliski, J., Ye, Y. 1993; 39: 757-776
  • Average performance of a self-dual interior-point algorithm for linear programming Complexity in Numerical Optimization Anstreicher, K., Ji, J., Potra, F., Ye, Y. edited by Pardalos, P. World Scientific, New Jersey. 1993: 1–15
  • Translation cuts for convex minimization Complexity in Numerical Optimization Burke, J., Goldstein, A., Tseng, P., Ye, Y. edited by Pardalos, P. World Scientific, New Jersey. 1993: 57–73
  • Solutions of $P_0$-matrix linear complementarity problems SIAM J. on Matrix Anal. Appl. Pardalos, P., Ye, Y., Han, C., Kaliski, J. 1993; 14: 1048-1060
  • Near-boundary behavior of the primal-dual potential reduction algorithm for linear programming Mathematical Programming Ye, Y., Kortanek, K., Kaliski, J., Huang, S. 1993; 58: 243-255
  • Minimal adjustment costs, factor demands, and seasonal time-of-use electricity rates Resource and Energy Economics Tishler, A., Ye, Y. 1993; 15: 313-335
  • An extension of the potential reduction algorithm for solving LCP with priority goals Linear Algebra and its Applications Kaliski, J., Ye, Y. 1993; 193: 35-50
  • A quadratically convergent polynomial interior-point algorithm for solving entropy optimization problems SIAM J. on Optimization Potra, F., Ye, Y. 1993; 3: 843-860
  • On quadratic and $O(\sqrt{n}L)$ convergence of a predictor- corrector algorithm for LCP Mathematical Programming Ye, Y., Anstreicher, K. 1993; 62: 537-552
  • On finding an interior point on the optimal face of linear programs Mathematical Programming Mehrotra, S., Ye, Y. 1993; 62: 497-516
  • On adaptive-step primal-dual interior-point algorithms for linear programming Mathematics of Operations Research Mizuno, S., Todd, M., Ye, Y. 1993; 18: 964-981
  • A quadratically convergent $O(\sqrt{n}L)$-iteration algorithm for linear programming Mathematical Programming Ye, Y., G\"uler, O., Tapia, R., Zhang, Y. 1993; 59: 151-162
  • A fully polynomial-time approximation algorithm for computing a stationary point of the general LCP Mathematics of Operations Research Ye, Y. 1993; 18: 334-345
  • Convergence behavior of some interior-point algorithms Mathematical Programming G\"uler, O., Ye, Y. 1993; 60: 215-228
  • Extensions of the potential reduction algorithm for linear programming Journal of Optimization Theory and Applications Ye, Y. 1992; 193: 35-50
  • On the Q-order of convergence of interior-point algorithms for linear programming Ye, Y. edited by Fang, W. 1992
  • A further result on potential reduction algorithm for the P-matrix linear complementarity problem Advances in Optimization and Parallel Computing, Ye, Y. edited by Pardalos, P. North-Holland, NY. 1992: 1
  • A new complexity result on minimization of a quadratic function with a sphere constraint Recent Advances in Global Optimization Ye, Y. edited by Floudas, C., Pardalos, P. Princeton University Press, NJ. 1992: 1
  • On affine scaling algorithms for nonconvex quadratic programming Mathematical Programming Ye, Y. 1992; 56: 285-300
  • Comparative analysis of affine scaling algorithms for linear programming Mathematical Programming Ye, Y. 1992; 52: 405-414
  • A potential reduction algorithm allowing column generation SIAM J. on Optimization Ye, Y. 1992; 2: 7-20
  • On the finite convergence of interior-point algorithms for linear programming Mathematical Programming Ye, Y. 1992; 57: 325-335
  • Implementation of interior-point algorithms for some entropy optimization problems Optimization Methods and Software Han, C., Pardalos, P., Ye, Y. 1992; 1: 71-80
  • An interior point potential reduction algorithm for the linear complementarity problem Mathematical Programming Kojima, M., Megiddo, N., Ye, Y. 1992; 54: 267-279
  • A class of LCPs solvable in polynomial time Linear Algebra and its Applications Ye, Y., Pardalos, P. 1991; 152: 3-17
  • Interior-point algorithms for quadratic programming Recent Developments in Mathematical Programming Ye, Y. edited by Kumar, S. Gordon \& Breach Scientific Publishers, Philadelphia. 1991: 1
  • On some efficient interior point methods for nonlinear convex programming Linear Algebra and its Applications Kortanek, K., Potra, F., Ye, Y. 1991; 152: 169-189
  • Interior-point algorithms for solving nonlinear optimization problems COAL Newsletter Han, C., Pardalos, P., Ye, Y. 1991; 19: 45-54
  • Convergence behavior of Karmarkar's projective algorithm for solving a simple linear program Operations Research Letters Kaliski, J., Ye, Y. 1991; 10: 389-393
  • An $O(n^3L)$ potential reduction algorithm for linear programming Mathematical Programming Ye, Y. 1991; 50: 239-258
  • Algorithms for the solution of quadratic knapsack problems Linear Algebra and its Applications Pardalos, P., Han, C., Ye, Y. 1991; 152: 69-91
  • A class of projective transformations for linear programming SIAM J. on Computing Ye, Y. 1990; 19: 457-466
  • Computational aspects of an interior point algorithm for quadratic programming problems with box constraints Large-Scale Numerical Optimization Han, C., Pardalos, P., Ye, Y. edited by Coleman, T., F., Li, Y. SIAM, Philadelphia. 1990: 1
  • Interior-point algorithms for global optimization Annals of Operations Research Ye, Y. 1990; 25: 59-74
  • Containing and shrinking ellipsoids in the path-following algorithm Mathematical Programming Ye, Y., Todd, M. 1990; 47: 1-9
  • A centered projective algorithm for linear programming Mathematics of Operations Research Todd, M., Ye, Y. 1990; 15: 508-529
  • A ``build-down'' scheme for linear programming Mathematical Programming Ye, Y. 1990; 46: 61-72
  • Recovering optimal basic variables in Karmarkar's polynomial algorithm for linear programming Mathematics of Operations Research Ye, Y. 1990; 15: 564-571
  • AN EXTENSION OF KARMARKAR PROJECTIVE ALGORITHM FOR CONVEX QUADRATIC-PROGRAMMING MATHEMATICAL PROGRAMMING Ye, Y. Y., Tse, E. 1989; 44 (2): 157-179
  • An extension of Karmarkar's projective algorithm for convex quadratic programming Mathematical Programming Ye, Y., Tse, E. 1989; 44: 157-179
  • Eliminating columns in the simplex method for linear programming Journal of Optimization Theory and Applications Ye, Y. 1989; 63: 103-111
  • RECOVERING OPTIMAL DUAL SOLUTIONS IN KARMARKARS POLYNOMIAL ALGORITHM FOR LINEAR-PROGRAMMING MATHEMATICAL PROGRAMMING Ye, Y. Y., Kojima, M. 1987; 39 (3): 305-317
  • KARMARKAR ALGORITHM AND THE ELLIPSOID METHOD OPERATIONS RESEARCH LETTERS Ye, Y. Y. 1987; 6 (4): 177-182
  • Recovering optimal dual solutions in Karmarkar's polynomial algorithm for linear programming Mathematical Programming Ye, Y., Kojima, M. 1987; 39: 305-317
  • A conclusion on `missing number' in ergodic exponents of $s\times s$ stochastic matrices Journal of Huazhong University of Science and Technology Ye, Y. 1983; 2
  • Directed graphs, linear Diophantine equations, and ergodic problems of stochastic matrices, English Edit. Journal of Huazhong University of Science and Technology Ye, Y. 1982; 2