叶荫宇

简历

叶荫宇(Yinyu Ye), 原斯坦福大学李国鼎讲席教授，现任上海交通大学和香港中文大学深圳访问讲习教授。他的主要研究方向为连续和离散优化，数据科学及应用，数字算法设计及分析，算法博弈及市场均衡，运筹及管理科学等; 他和其他科学家开创了内点优化算法，锥规划模型，分布式鲁棒优化，在线线性规划和学习，强化学习和马可夫过程及非凸优化算法分析等。他和他的学生多次获得科学奖项:包括他自己的2006INFORMSFarkasPrize(首位获奖者)，2009年约翰·冯·洛伊曼理论奖，国际数学规划2012TsengLectureshipPrize(首位获奖者每三年)，2014美国应用数学学会优化奖(每三年)等。根据谷歌学术统计，目前他的文章被引用总计超过60,000次。

教育经历

• Huazhong University of Science and Technology, 1978-09 ~ 1982-07, Automation, Bachelor
• Stanford University, 1982-09 ~ 1983-07, Engineering and Economic Systems, Master
• Stanford University, 1983-09 ~ 1988-07, Operations Research and Engineering-Economic Systems, Ph.D.

工作经历

• University of Iowa, 1988-09 ~ 1990-08, Assistant Professor
• University of Iowa, 1990-09 ~ 1993-08, Associate Professor
• University of Iowa, 1993-09 ~ 1998-01, Professor
• Stanford University, 2002-04 ~ 2024-8-30, Chair Professor

荣誉和获奖

论著

Refereed Journal Papers

• [J196] “Optimal diagonal preconditioning,” (Zhaonan Qu, Wenzhi Gao, Oliver Hinder, Yinyu Ye, Zhengyuan Zhou), Operations Research (Online), 2024.
• [J195] “A gradient descent akin method for constrained optimization: algorithms and applications,” (Long Chen, Kai Uwe Bletzinger, Nicolas R Gauger, Yinyu Ye), Optimization Methods and Software, 2024, pp 1-28.
• [J194] “Worst-case iteration bounds for log barrier methods on problems with nonconvex constraints,” (Oliver Hinder, Yinyu Ye), Mathematics of Operations Research, 2023.
• [J193] “Fisher markets with linear constraints: Equilibrium properties and efficient distributed algorithms,” (Devansh Jalota, Marco Pavone, Qi Qi, Yinyu Ye), Games and Economic Behavior 141, 2023, pp 223-260.
• [J192] “Optimization of asset allocation and liquidation time in investment decisions with VaR as a risk measure,” (Chunhui Xu, Yinyu Ye), Computational Economics, 2023, pp 1-27.
• [J191] “Variance reduced value iteration and faster algorithms for solving Markov decision processes,” (Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye), Naval Research Logistics (NRL) 70(5), 2023, pp423-442.
• [J190] “An Improved Analysis of LP-Based Control for Revenue Management,” (Guanting Chen, Xiaocheng Li, Yinyu Ye), Operations Research (Online), 2022, pp 1-15.
• [J189] “Optimization and Operations Research in Mitigation of a Pandemic,” (Cai-Hua Chen, Yu-Hang Du, Dong Dong Ge, Lin Lei, Yinyu Ye), Journal of the Operations Research Society of China, 2022, pp1-18.
• [J188] “Managing randomization in the multi-block alternating direction method of multipliers for quadratic optimization,” (Krešimir Mihić, Mingxi Zhu, Yinyu Ye), Mathematical Programming Computation, 13(2), 2021, pp339-413.
• [J187] “On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods,” (Gabriel Haeser, Oliver Hinder, Yinyu Ye), Mathematical Programming, 186(1), 2021, pp257-288.
• [J186] “An ADMM-based interior-point method for large-scale linear programming,” (Tianyi Lin, Shiqian Ma, Yinyu Ye, Shuzhong Zhang), Optimization Methods and Software, 36(2-3), 2021/5/4, pp389-424.
• [J185] “Online linear programming: Dual convergence, new algorithms, and regret bounds,” (Xiaocheng Li and Ye), Operations Research, Published Online: 30 Nov 2021.
• [J184] “Worst-case complexity of cyclic coordinate descent: O ( n 2 ) gap with randomized version,” (Ruoyu Sun, Yinyu Ye), Mathematical Programming, 185(1), 2021, pp487-520.
• [J183] “MULTILEVEL MONTE CARLO SAMPLING ON HETEROGENEOUS COMPUTER ARCHITECTURES,” (Christiane Adcock, Yinyu Ye, Lluis Jofre, Gianluca Iaccarino), International Journal for Uncertainty Quantification 10(6), pages 575-594 (2020).
• [J182] “Managing randomization in the multi-block alternating direction method of multipliers for quadratic optimization,” (K Mihić, M Zhu, Y Ye), Mathematical Programming Computation, 1-75 (2020).
• [J181] “An ADMM-based interior-point method for large-scale linear programming,” (Tianyi Lin, Shiqian Ma, Yinyu Ye, Shuzhong Zhang), Optimization Methods and Software, 1-36 (2020).
• [J180] “Towards solving 2-TBSG efficiently,” (Z Jia, Z Wen, Y Ye), Optimization Methods and Software 35 (4), 706- 721 (2020).
• [J179] “On the complexity of an expanded Tarski’s fixed point problem under the componentwise ordering,” (Chuangyin Dang, Yinyu Ye), Theoretical Computer Science 732 pp26-45 (2018).
• [J178] “Approximation Hardness for A Class of Sparse Optimization Problems,” (Yichen Chen, Yinyu Ye, Mengdi Wang), Journal of Machine Learning Research 20 (2019) 1-27.
• [J177] “On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods,” (Gabriel Haeser, Oliver Hinder and Yinyu Ye), Math. Program, 1-32 (2019/12/21).
• [J176] “Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs,” (Samuel Burer and Yinyu Ye), Math. Program, 1-17 (2018/2/7).
• [J175] “Worst-case Complexity of Cyclic Coordinate Descent: $O (n^2)$ Gap with Randomized Version,” (R Sun, Y Ye), appeared in Math Prog. 2019.
• [J174] “On the Efficiency of Random Permutation for ADMM and Coordinate Descent,” (Sun, LUO and Ye), Math. of OR online, 2019.
• [J173] “Optimality condition and complexity analysis for linearly-constrained optimization without differentiability on the boundary,” (Haeser, G., Liu, H. & Ye, Y.) Mathematical Programming 178 (1), 263-299 (2018).
• [J172] “Sample Average Approximation with Sparsity-Inducing Penalty for High-Dimensional Stochastic Programming,” (Hongcheng Liu, Xue Wang, Tao Yao, Runze Li, Ye), Mathematical programming 178 (1), 69-108 (2019).
• [J171] “On Doubly Positive Semidefinite Programming Relaxations,” (Fu, Ge and Ye), Journal of Computational Mathematics, Vol.36, No.3, 391–403 (2018).
• [J170] “A computation study on an integrated alternating direction method of multipliers for large scale optimization,” (Masoud Zarepisheh, Lei Xing, Yinyu Ye), Optimization Letters, 12(1), 3-15 (2018).
• [J169] “Extended ADMM and BCD for nonseparable convex minimization models with quadratic coupling terms: convergence analysis and insights,” (C Chen, M Li, X Liu, Y Ye), Mathematical Programming 173 (1-2), 37-77 (2019)
• [J168] “Assessing the System Value of Optimal Load Shifting,” (James Merrick, Yinyu Ye, and Robert Entriken), IEEE Transactions on Smart Grid 9 (6), 5943-5952, 2018.
• [J167] “Folded Concave Penalized Sparse Linear Regression: Sparsity, Statistical Performance, and Algorithmic Theory for Local Solutions,” (Hongcheng Liu, Tao Yao, Runze Li, Ye), Mathematical programming 166 (1-2), 207- 240, 2017
• [J166] “On a New SDP-SOCP Method for Acoustic Source Localization Problem,” (Mingjie Gao, Ka-Fai Cedric Yiu, Sven Nordholm, Yinyu Ye), ACM Transactions on Sensor Networks (TOSN) 12 (4) 2016/10/25, pp 36-
• [J165] “A Mathematical Formulation for Optimal Load Shifting of Electricity Demand for the Smart Grid,” (Hu, Skorupski, Entriken and Ye), IEEE Transactions on Big Data, 2016/12/15,
• [J164] “An Integrated Alternating Direction Method of Multipliers for Treatment Planning Optimization,” (M Zarepisheh, Y Ye, L Xing) Medical physics 42 (6) (2015) 3532-3532.
• [J163] “Likelihood robust optimization for data-driven problems,” (Zizhuo Wang , Peter W. Glynn, Yinyu Ye) Computational Management Science, Online (September 2015) 1-21.
• [J162] “A fixed point iterative approach to integer programming and its distributed computation,” (Dang and Ye), Fixed Point Theory and Applications, 2015(1), 1-15.
• [J161] “The simplex method is strongly polynomial for deterministic Markov decision processes,” (Ian Post and Ye), Math of Operations Research, 40 (4) (2015) 859-868.
• [J160] “Linear operators and positive semidefiniteness of symmetric tensor spaces,” (Luo, Qi and Ye), Science China Mathematics, 58(1) (2015) 197-212.
• [J159] “The Direct Extension of ADMM for Multi-block Convex Minimization Problems is Not Necessarily Convergent,” (Caihua Chen, Bingsheng He, Yinyu Ye, Xiaoming Yuan), Math Programming. 155(1-2) (2016) 57-79.
• [J158] “Hidden-City Ticketing: the Cause and Impact,” (Wang and Ye), Transportation Science, 50(1) (2016) 288-305.
• [J157] “The Value of Stochastic Modeling in Two-Stage Stochastic Programs with Cost Uncertainty,” (Delage, Arroyo and Ye), Operations Research, 62 (6) (2014) 1377-1393.
• [J156] “Complexity Analysis of Interior Point Algorithms for Non-Lipschitz and Nonconvex Minimization,” (W. Bian, X. Chen, and Ye), Math Programming, 149 (2015) 301-327.
• [J155] “Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)” (M Zarepisheh, Y Ye, S Boyd, R Li, L Xing), Medical Physics 41(6) (2014) 292-292.
• [J154] “Waterflood management using two-stage optimization with streamline simulation” (T Wen, MR Thiele, DE Ciaurri, K Aziz, Y Ye), Computational Geosciences, February (2014) 1-22.
• [J153] “A Dynamic Near-Optimal Algorithm for Online Linear Programming” (Agrawal, Wang and Ye), Operations Research, 62(4) (2014) 876 – 890.
• [J152] “A Homogeneous Interior-Point Algorithm for Nonsymmetric Convex Conic Optimization,” (Anders Skajaa and Ye), Math Programming, May (2014) 1-32.
• [J151] “Competitive Communication Spectrum Economy and Equilibrium,” Journal of the Operations Research Society of China, 2(1) (2014) 1-16,
• [J150] “Close the Gaps: A Learning-while-Doing Algorithm for a Class of Single-Product Revenue Management Problems,” (Wang, Deng and Ye), Operations Research, 62(2) (2014) 318-331.
• [J149] “Analytical results and efficient algorithm for optimal portfolio deleveraging with market impact,” (Jingnan Chen, Liming Feng, Jiming Peng, Yinyu Ye), Operations Research, 62(1) (2014) 195-206.
• [J148] “A Behavioral Model of “Muddling Through” in the Chinese Bureaucracy: The Case of Environmental Protection,” (Xueguang Zhou, Hong Lian, Leonard Ortolano, Yinyu Ye), China Journal, 70 (2013) 120-147.
• [J147] “A Levenberg-Marquardt method with approximate projections,” (R Behling, A Fischer, M Herrich, A Iusem, Y Ye), Computational Optimization and Applications, 59 (1-2) (2014) 5-26.
• [J146] “Space tensor conic programming,” (L Qi and Y Ye), Computational Optimization and Applications, 6(26) (2013) 1-13.
• [J145] “A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles,” (Nicole Taheri, Robert Entriken, Yinyu Ye), IEEE Transactions on Smart Grid, 4(4) (2013) 1772-1779.
• [J144] “Complexity of Unconstrained L2-Lp Minimization,” (Chen, Ge, Wang, Ye), Math Programming, 143 (1-2) (2014) 371-383
• [J143] “Newsvendor Optimization with Limited Distribution Information,” (Zhu, Zhang and Ye), Optimization Methods and Software 28(3) (2013) 640-667
• [J142] “Warmstarting the Homogeneous and Self-Dual Interior Point Method for Linear and Conic Quadratic Problems,” (Anders Skajaa, Erling D. Andersen and Yinyu Ye), Math Programming Computation; 5(1) (2013) 1-25.
• [J141] “On affine motions and bar frameworks in general position,” (Alfakih and Ye), Linear Algebra and Applications, 438 (1) (2013) 31–36.
• [J140] “A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks” (Fleming, Maes, Saunders, Ye, Palsson), Journal of Theoretical Biology, 292 (2012) 71-77.
• [J139] “Price of Correlations in Stochastic Optimization” (Agrawal, Ding, Sebari, and Ye), Operations Research, 60:1 (2012), 150-162.
• [J138] “The cubic spherecial optimization problem,” (Zhang, Qi and Ye), Math of Computation, 81 (2012) 1513.
• [J137] “On stress matrices of (d + 1)-lateration frameworks in general position,” (Alfakih, Taheri, and Ye), Mathematical Programming 137 (1-2) (2013), 1-17.
• [J136] “The Simplex and Policy-Iteration Methods are Strongly Polynomial for the Markov Decision Problem with a Fixed Discount Rate,” Math Operations Res, 36:4 (2011) 593-603.
• [J135] “A Note on Complexity of L-p Minimization” (Ge, Jiang and Ye), Math Programming, 129:2 (2011) 285-299.
• [J134] “A Unified Framework for Dynamic Prediction Market Design,” (Agrawal, Delage, Peters, Wang, and Ye), Operations Research, 59:3 (2011) 550-568.
• [J133] “Lower Bound Theory of Nonzero Entries in Solutions of L2-Lp Minimization” (Chen, Xu and Ye), SIAM J. Scientific Computing 32:5 (2010) 2832-2852.
• [J132] “Semidefinite Relaxation of Quadratic Optimization Problems,” (Luo, Ma, So, Ye, and Zhang), IEEE Signal Processing Magazine 27:3 (2010) 20-34.
• [J131] “Universal Rigidity and Edge Sparsification for Sensor Network Localization” (Zhu, So and Ye), SIAM J Optimization 20(6) (2010) 3059-3081.
• [J130] “An interior-point path-following algorithm for computing a Leontief economy equilibrium,” (Dang, Zhu and Ye), Computational Optimization and Applications 50:2 (2011) 223-236.
• [J129] “A FPTAS for Computing a Symmetric Leontief Competitive Economy Equilibrium,” (Zhu, Dang and Ye), Math Programming 131 (2012) 113–129
• [J128] “Statistical ranking and combinatorial Hodge theory.” (Jiang, Lim, Yao, and Ye), Math Programming 127:1 (2011) 203-244.
• [J127] “A novel fluence map optimization model incorporating leaf sequencing constraint,” (Jin, Min, Song, Liu, and Ye), Physics in Medicine and Biology 55 (2010) 1243-1264.
• [J126] “Geometric Rounding: A Dependent Randomized Rounding Scheme,” (Ge, He, Ye, and Zhang), Journal of Combinatorial Optimization 22:4 (2011) 699-725.
• [J125] “A Note on Equilibrium Pricing as Convex Optimization” (Chen, Ye, Zhang), Journal of Computational Mathematics 28:5 (2010) 569–578.
• [J124] “Dynamic Spectrum Management with the Competitive Market Model,” (Yao, Armbruster, and Ye), IEEE Tran on Signal Processing 58:4 (2010) 2442-2446.
• [J123] “Conceptual formulation on four-dimensional inverse planning for intensity modulated radiation therapy,” (Lee, Ma, Ye and Xing), Physics in Medicine and Biology 54 (2009) 255-266.
• [J122] “Bi-Quadratic Optimization over Unit Spheres and Semidefinite Programming Relaxations,” (Ling, Nie, Qi and Ye ), SIAM J. Optimization 20:3 (2010) 1286-1310.
• [J121] “Stochastic Combinatorial Optimization with Controllable Risk Aversion Level,” (So, Zhang and Ye), Math Operations Res 34:3 (2009) 522-537.
• [J120] “Distributionally Robust Optimization under Moment Uncertainty with Application to Data-Driven Problems,” (Delage and Ye), Operations Research 58:3 (2009) 595-612.
• [J119] “DSDP5—software for semidefinite programming,” (Benson and Ye), ACM Tran on Mathematical Software 34:3 (2008).
• [J118] “Budget Allocation in a Competitive Communication Spectrum Economy,” (Lin, Tsai and Ye), EURASIP J. Advances in Signal Processing 2009:1 (2009).
• [J117] “An edge-reduction algorithm for the vertex cover problem,” (Han, Punnen and Ye), Operations Research Letters 37 (2009) 181-186.
• [J116] “Using total-variation regularization for segment-based dose optimization with field specific numbers of segments,” (Zhu, Lee, Ma, Ye, Mazzeo and Xing), Physics in Medicine and Biology 53 (2008) 6653-6672.
• [J115] “The complexity of equilibria: Hardness results for economies via a correspondence with games” (Codenotti, Saberi, Varadarajan and Ye), Theoretical Computer Science 408:2-3 (2008) 188-198.
• [J114] “A Unified Theorem on SDP Rank Reduction,” (So, Ye and Zhang), Math Operations Res 33:4 (2008) 910-920.
• [J113] “Further Relaxations of the SDP Approach to Sensor Network Localization,” (Wang, Song, Boyd and Ye), SIAM J. Optimization 19 (2008) 655-673.
• [J112] “A Distributed SDP approach for Large-scale Noisy Anchor-free Graph Realization with Applications to Molecular Conformation,” (Biswas, Toh and Ye), SIAM J. Scientific Computing, 30:3 (2008) 1251-1277.
• [J111] “Finding Equitable Convex Partitions of Points in a Polygon Efficiently,” (Carlsson, Armbruster and Ye), ACM Tran on Algorithms 6:4 (2010).
• [J110] “Exchange Market Equilibria with Leontief’s Utility: Freedom of Pricing Leads to Rationality,” Theoretical Computer Science 378:2 (2007) 134-142.
• [J109] “SPASELOC: An Adaptive Subproblem Algorithm for Scalable Wireless Sensor Network Localization,” (Carter, Jin, Saunders, and Ye), SIAM J. Optimization 17:4 (2006) 1102-1128.
• [J108] “Approximation Algorithms for Metric Facility Location Problems,” (Mahdian, Ye and Zhang), SIAM J. Computing 36:2 (2006) 411-432.
• [J107] “Semidefinite Programming Based Algorithms for Sensor Network Localization,” (Biswas, Liang, Wang and Ye), ACM Tran on Sensor Networks 2:2 (2006) 188-220.
• [J106] “Semidefinite Programming Approaches for Sensor Network Localization with Noisy Distance Measurements,” (Biswas, Liang, Toh, Wang and Ye), IEEE Tran on Automation Science and Engineering 3:4 (2006) 360-371.
• [J105] “An Improved Algorithm for Approximating the Radii of Point Sets,” (Varadarajan, Venkatesh, Ye and Zhang), SIAM J. Computing 36:6 (2007) 1764-1776.
• [J104] “On Approximating Complex Quadratic Optimization Problems via Semidefinite Programming,” (So, Zhang and Ye), Math Programming 110:1 (2007) 93-110.
• [J103] “Disciplined convex programming,” (Michael Grant, Stephen Boyd, Yinyu Ye), Global Optimization 84 (2006) 155-210.
• [J102] “Lot-sizing scheduling with batch setup times,” (Chen, Ye and Zhang), Journal of Scheduling 9:3 (2006) 299- 310.
• [J101] “A Path to the Arrow-Debreu Competitive Market Equilibrium,” Math Programming, 111:1-2 (2008) 315-348.
• [J100] “On Solving Fewnomials Over Intervals in Fewnomial Time,” (Rojas and Ye), Journal of Complexity 21:1 (2005) 87-110.
• [J99] “A new complexity result on solving the Markov decision problem,” Math Operations Res 30:3 (2005) 733-749.
• [J98] “Improved complexity results on solving real-number linear feasibility problems,” Math Programming 106:2 (2006) 339-363.
• [J97] “Theory of Semidefinite Programming for Sensor Network Localization,” (So and Ye), Math Programming 109:2-3 (2007) 367-384.
• [J96] “A Multi-Exchange Local Search Algorithm for the Capacitated Facility Location Problem,” (Zhang, Chen and Ye), Math Operations Research 30:2 (2005) 389-403.
• [J95] “Improved Combinatorial Approximation Algorithms for the k-Level Facility Location Problem,” (Ageev, Ye and Zhang), SIAM J. Discrete Math 18:1 (2004) 207-217.
• [J94] “New results on quadratic minimization,” (Zhang and Ye), SIAM J. Optimization 14:1 (2003) 245-267.
• [J93] “Approximate the 2-Catalog Segmentation Problem Using Semidefinite Programming Relaxations,” (Xu, Ye and Zhang), Optimization Methods and Software 18:6 (2003) 705-719.
• [J92] “On some interior-point algorithms for nonconvex quadratic optimization”, (Tseng and Ye), Math Programming 93 (2003) 217-225.
• [J91] “Approximation of dense-n/2-subgraph and the complement of min-bisection,” (Ye and Zhang), Journal of Global Optimization 25:1 (2003) 55-73.
• [J90] “A Note on the Maximization Version of Multi-level Facility Location Problems,” (Zhang and Ye), Operations Research Letters 30:5 (2002) 333-335.
• [J89] “Optimization with a few violated constraints,” (Bai, Cho, Tempo and Ye), IEEE Tran on Automatic Control 47:7 (2002) 1067-1077.
• [J88] “An Approximation Algorithm for Scheduling Two-Parallel Machines with Capacity Constraints,” (Yang, Ye and Zhang), Discrete and Applied Mathematics 130:3 (2003) 449-467.
• [J87] “Improved Approximation for Max Set Splitting and Max NAE SAT,” (Zhang, Ye, and Han), Discrete and Applied Mathematics 142:1-3 (2004) 133-149.
• [J86] “On approximation of Max-Vertex-Cover,” (Han, Ye, Zhang and Zhang), European J Operations Research 143:2 (2002) 342-355.
• [J85] “Blind channel equalization using -approximation algorithms,” (Li, Bai and Ye), IEEE Tran on Signal Processing 49:11 (2001) 2823-2831.
• [J84] “An improved rounding method and semidefinite relaxation for graph partition,” (Han, Ye, and Zhang), Math Programming 92:3 (2002) 509-535.
• [J83] “A .699 approximation algorithm for Max-Bisection,” Math Programming 90:1 (2001) 101-111.
• [J82] “Characterizations, bounds, and probabilistic analysis of two complexity measures for linear programming problems,” (Todd, Tuncel, Ye), Math Programming 90:1 (2001) 59-70.
• [J81] “Convergence results of analytic center estimator,” (Bai, Fu, Tempo, and Ye), IEEE Tran on Automatic Control 45:3 (2000) 569-572.
• [J80] “On smoothing methods for the P0 matrix linear complementarity problem,” (Chen and Ye), SIAM J. Optimization 11 (2001) 341-363.
• [J79] “Solving large-scale sparse semidefinite programs for combinatorial optimization,” (Benson, Ye, and Zhang), SIAM J. Optimization 10 (2000) 443-461.
• [J78] “An efficient algorithm for minimizing a sum of P-norms,” (Xue and Ye), SIAM J. Optimization 10 (2000) 551- 579.
• [J77] “Mixed linear and semidefinite programming for combinatorial and quadratic optimization,” (Benson, Ye, and Zhang), Optimization Methods and Software 11&12 (1999) 515-544.
• [J76] “Approximating global quadratic optimization with convex quadratic constraints,” Journal of Global Optimization 15 (1999) 1-17.
• [J75] “Approximating quadratic programming with bound and quadratic constraints,” Math Programming 84 (1999) 219-226.
• [J74] “On homotopy-smoothing methods for box constrained variational inequalities,” (Chen and Ye), SIAM J. Control & Optimization 37 (1999) 589-616.
• [J73] “On the quadratic convergence of the O(n .5L)-iteration homogeneous and self-dual linear programming algorithm,” (Wu, Wu, and Ye), Annals of Operations Research 87 (1999) 393-406.
• [J72] “A computational study of the homogeneous algorithm for large-scale convex optimization,” (Andersen and Ye), Computational Optimization and Applications 10 (1998) 243-269.
• [J71] “Probabilistic analysis of an infeasible interior-point algorithm for linear programming,” (Anstreicher, Ji, Potra and Ye), Math Operations Res 24 (1999) 176-192.
• [J70] “Infeasible-start primal-dual methods and infeasibility detectors for nonlinear programming problems,” (Nesterov, Todd, and Ye), Math Programming 84 (1999) 227-267.
• [J69] “Constrained logarithmic least squares in parameter estimation,” (Bai and Ye), IEEE Tran on Automatic Control 44:1 (1999) 182-185.
• [J68] “Approximation algorithms for quadratic programming,” (Fu, Luo, and Ye), Journal of Combinatorial Optimization 2:1 (1998) 29-50.
• [J67] “On the complexity of approximating a KKT point of quadratic programming,” Math Programming 80 (1998) 195-212.
• [J66] “On a homogeneous algorithm for the monotone complementarity problem,” (Andersen and Ye), Math Programming 84 (1999) 375-400.
• [J65] “Bounded error parameter estimation: a sequential analytic center approach,” (Bai, Ye and Tempo), IEEE Tran on Automatic Control 44:6 (1999) 1107-1117.
• [J64] “How partial knowledge helps to solve linear programs,” Journal of Complexity 12 (1996) 480-491.
• [J63] “Approximate Farkas lemmas and stopping rules for iterative infeasible-point algorithms for linear programming,” (Todd and Ye), Math Programming 81 (1998) 1-22.
• [J62] “Efficient algorithms for minimizing a sum of Euclidean norms with applications,” (Xue and Ye), SIAM J. Optimization 7 (1997) 1017-1036.
• [J61] “Complexity analysis of the analytic center cutting plane method that uses multiple cuts,” Math Programming 78 (1997) 85-104.
• [J60] “On the relationship between layered least squares and affine scaling steps,” (Vavasis and Ye), Lectures in Applied Mathematics 32 (1996) 857-866.
• [J59] “An infeasible interior-point algorithm for solving primal and dual geometric programs,” (Kortanek, Xu, and Ye), Math Programming 76 (1997) 155-182.
• [J58] “A primal-dual interior-point method whose running time depends only on the constraint matrix,” (Vavasis and Ye), Math Programming 74 (1996) 79-120.
• [J57] “On homogeneous and self-dual algorithm for LCP,” Math Programming 76 (1997) 211-222.
• [J56] “Combining interior-point and pivoting algorithms for linear programming,” (Andersen and Ye), Management Science 42 (1996) 1719-1731.
• [J55] “Improved complexity using higher-order correctors for primal-dual Dikin affine scaling,” (Jansen, Roos, Terlaky and Ye), Math Programming 76 (1997) 117-130.
• [J54] “Interior-point methods for nonlinear complementarity problem,” (Potra and Ye), Journal of Optimization Theory and Application 88 (1996) 617-642.
• [J53] “A lower bound on the number of iterations of long-step and polynomial interior-point linear programming algorithms,” (Todd and Ye), Annals of Operations Research 62 (1996) 233-252.
• [J52] “A convergent algorithm for quantile regression with smoothing splines,” (Bosch, Ye, and Woodworth), Computational Statistics & Data Analysis 19 (1995) 613-630.
• [J51] “A asymptotical O(n .5L)-iteration path-following linear programming algorithm that uses long steps,” (Hung and Ye), SIAM J. Optimization 6 (1996) 570-586.
• [J50] “A generalized homogeneous and self-dual linear programming algorithm,” (Xu and Ye), Operations Research Letters 17:2 (1995) 181-190.
• [J49] “A simplification of the homogeneous and self-dual linear programming algorithm and its implementation,” (Xu, Hung and Ye), Annals of Operations Research 62 (1996) 151-172.
• [J48] “Condition numbers for polyhedra with real number data,” (Vavasis and Ye), Operations Research Letters 17 (1995) 209-214.
• [J47] “Identifying an optimal basis in linear programming,” (Vavasis and Ye), Annals of Operations Research 62 (1996) 565-572.
• [J46] “On the von Neumann economic growth problem,” Math Operations Res 20 (1995) 617-633.
• [J45] “Complexity analysis of an interior-point cutting plane method for convex feasibility problem,” (Goffin, Luo and Ye), SIAM J. Optimization 6 (1996) 638-652.
• [J44] “Specially structured uncapacitated facility location problems,” (Jones, Lowe, Muller, Xu, Ye, and Zydiak), Operations Research 43 (1995) 661-669.
• [J43] “A surface of analytic centers and infeasible-interior-point algorithms for linear programming,” (Mizuno, Todd, and Ye), Math Operations Res 20 (1995) 135-162.
• [J42] “An O(n .5L)-iteration homogeneous and self-dual linear programming algorithm,” (Ye, Todd and Mizuno), Math Operations Res 19 (1994) 53-67.
• [J41] “Combining binary search and Newton’s method to compute real roots for a class of real functions,” Journal of Complexity 10 (1994) 271-280.
• [J40] “Toward probabilistic analysis of interior-point algorithms for linear programming,” Math Operations Res 19 (1994) 38-52.
• [J39] “On the convergence of the iteration sequence in primal-dual interior-point methods,” (Tapia, Zhang and Ye), Math Programming 68 (1995) 141-154.
• [J38] “On quadratic and O(n .5L) convergence of a predictor-corrector algorithm for LCP,” (Ye and Anstreicher), Math Programming 62 (1993) 537-552.
• [J37] “A complexity analysis for interior-point algorithms based on Karmarkar’s potential functions,” (Ji and Ye), SIAM J. Optimization 4 (1994) 512-520.
• [J36] “The optimal choice of inputs under time of use pricing, fixed proportions technology and adjustment costs: an application to industrial firms,” (Spector, Tishler and Ye), Management Sciences 41 (1995) 1679-1692.
• [J35] “A short-cut potential reduction algorithm for linear programming,” (Kaliski and Ye), Management Science 39 (1993) 757-776.
• [J34] “Minimal adjustment costs, factor demands, and seasonal time-of-use electricity rates,” (Tishler and Ye), Resource and Energy Economics 15 (1993) 313-335.
• [J33] “On finding an interior point on the optimal face of linear programs,” (Mehrotra and Ye), Math Programming 62 (1993) 497-516.
• [J32] “On the finite convergence of interior-point algorithms for linear programming,” Math Programming 57 (1992) 325-335.
• [J31] “A quadratically convergent O(n .5L)-iteration algorithm for linear programming,” (Ye, Guler, Tapia and Zhang), Math Programming 59 (1993) 151-162.
• [J30] “Convergence behavior of some interior-point algorithms,” (Guler and Ye), Math Programming 60 (1993) 215- 228.
• [J29] “A fully polynomial-time approximation algorithm for computing a stationary point of the general LCP,” Math Operations Res 18 (1993) 334-345.
• [J28] “A quadratically convergent polynomial interior-point algorithm for solving entropy optimization problems,” (Potra and Ye), SIAM J. Optimization 3 (1993) 843-860.
• [J27] “On adaptive-step primal-dual interior-point algorithms for linear programming,” (Mizuno, Todd and Ye), Math Operations Res 18 (1993) 964-981.
• [J26] “Implementation of interior-point algorithms for some entropy optimization problems,” (Han, Pardalos and Ye), Optimization Methods and Software 1 (1992) 71-80.
• [J25] “Solutions of P0-matrix linear complementarity problems,” (Pardalos, Ye, Han and Kaliski), SIAM J. Matrix Anal. Appl. 14 (1993) 1048-1060.
• [J24] “Near-boundary behavior of the primal-dual potential reduction algorithm for linear programming,” (Ye, Kortanek, Kaliski and Huang), Math Programming 58 (1993) 243-255.
• [J23] “A potential reduction algorithm allowing column generation,” SIAM J. Optimization 2 (1992) 7-20.
• [J22] “Convergence behavior of Karmarkar’s projective algorithm for solving a simple linear program,” (Kaliski and Ye), Operations Research Letters 10 (1991) 389-393.
• [J21] “Comparative analysis of affine scaling algorithms for linear programming,” Math Programming 52 (1992) 405- 414.
• [J20] “An extension of the potential reduction algorithm for solving LCP with priority goals,” (Kaliski and Ye) Linear Algebra and its Applications 193 (1993) 35-50.
• [J19] “On affine scaling algorithms for nonconvex quadratic programming,” Math Programming 56 (1992) 285-300.
• [J18] “Extensions of the potential reduction algorithm for linear programming,” Journal of Optimization Theory and Applications 72 (1992) 487-498.
• [J17] “On some efficient interior point methods for nonlinear convex programming,” (Kortanek, Potra and Ye), Linear Algebra and its Applications 152 (1991) 169-189.
• [J16] “Interior-point algorithms for global optimization,” Annals of Operations Research 25 (1990) 59-74.
• [J15] “A class of LCPs solvable in polynomial time,” (Ye and Pardalos), Linear Algebra and its Applications 152 (1991) 3-17.
• [J14] “Algorithms for the solution of quadratic knapsack problems,” (Pardalos, Han and Ye), Linear Algebra and its Applications 152 (1991) 69-91.
• [J13] “Containing and shrinking ellipsoids in the path-following algorithm,” (Ye and Todd), Math Programming 47 (1990) 1-9.
• [J12] “A class of projective transformations for linear programming,” SIAM J. Computing 19 (1990) 457-466.
• [J11] “An O(n3L) potential reduction algorithm for linear programming,” Math Programming 50 (1991) 239-258.
• [J10] “An interior point potential reduction algorithm for the linear complementarity problem,” (Kojima, Megiddo and Ye), Math Programming 54 (1992) 267-279.
• [J9] “A centered projective algorithm for linear programming,” (Todd and Ye), Math Operations Res 15 (1990) 508- 529.
• [J8] “Recovering optimal basic variables in Karmarkar’s polynomial algorithm for linear programming,” Math Operations Res 15 (1990) 564-571.
• [J7] “A build-down' scheme for linear programming,” Math Programming 46 (1990) 61-72. [J6] “An extension of Karmarkar's projective algorithm for convex quadratic programming,” (Ye and Tse) Math Programming 44 (1989) 157-179. [J5] “Eliminating columns in the simplex method for linear programming,” Journal of Optimization Theory and Applications 63 (1989) 103-111. [J4] “Karmarkar's algorithm and the ellipsoid method,” Operations Research Letters 4 (1987) 177-182. [J3] “Recovering optimal dual solutions in Karmarkar's polynomial algorithm for linear programming,” (Ye and Kojima), Math Programming 39 (1987) 305-317. [J2] “A conclusion onmissing number’ in ergodic exponents of s × s stochastic matrices,” Journal of Huazhong University of Science and Technology 2 (1983).
• [J1] “Directed graphs, linear Diophantine equations, and ergodic problems of stochastic matrices,” English Edit. Journal of Huazhong University of Science and Technology 2 (1982).

Books/Monographs

• [C63] Linear and Nonlinear Programming, 5th edition, (David Luenberger and Yinyu Ye), Springer, International Series in Operations Research and Management Science, 2022.
• [C44] Linear and Nonlinear Programming, 3rd edition, (David Luenberger and Yinyu Ye), Springer, International Series in Operations Research and Management Science, 2008.
• [C15] Interior-Point Algorithms: Theory and Analysis, Wiley-Interscience Series in Discrete Mathematics and Optimization, John Wiley & Sons, Monograph, 1997.

Refereed Conference Proceeding/Book Chapters Papers

Archival Publications

• [C86] “Sketched Newton Value Iteration for Large-Scale Markov Decision Processes,” (Jinsong Liu, Chenghan Xie, Qi Deng, Dongdong Ge, Yinyu Ye), Proceedings of the AAAI Conference on Artificial Intelligence, 2024/3/24.
• [C85] “Trust region methods for nonconvex stochastic optimization beyond Lipschitz smoothness,” (Chenghan Xie, Chenxi Li, Chuwen Zhang, Qi Deng, Dongdong Ge, Yinyu Ye), Proceedings of the AAAI Conference on Artificial Intelligence, 2024/3/24.
• [C84] “Learning to Pivot as a Smart Expert,” (Tianhao Liu, Shanwen Pu, Dongdong Ge, Yinyu Ye), Proceedings of the AAAI Conference on Artificial Intelligence, 2024/3/24.
• [C83] “Solving linear programs with fast online learning algorithms,” (Wenzhi Gao, Dongdong Ge, Chunlin Sun, Yinyu Ye), International Conference on Machine Learning, 2023, pp 10649-10675.
• [C82] “Fine-grained correlation loss for regression,” (Chaoyu Chen, Xin Yang, Ruobing Huang, Xindi Hu, Yankai Huang, Xiduo Lu, Xinrui Zhou, Mingyuan Luo, Yinyu Ye, Xue Shuang, Juzheng Miao, Yi Xiong, Dong Ni), International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022, pp 663-672.
• [C81] “How Small Amount of Data Sharing Benefits Distributed Optimization and Learning,” (M Zhu and Y Ye), NeurIPS Workshop: Order up! The Benefits of Higher-Order Optimization in Machine Learning, Best Paper, 2022.
• [C80] “DRSOM: A Dimension Reduced Second-Order Method,” (Chuwen Zhang; Jiang Bo; Chang He; Yuntian Jiang; Dongdong Ge and Y Ye), NeurIPS Workshop: Order up! The Benefits of Higher-Order Optimization in Machine Learning, Spotlight Presentation, 2022.
• [C79] “The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks,” International Conference on Machine Learning, Long Presentation, 2021.
• [C78] “Markets for Efficient Public Good Allocation with Social Distancing,” (D Jalota, M Pavone, Q Qi, Y Ye), International Conference on Web and Internet Economics, 102-116, 2020.
• [C77] “Solving discounted stochastic two-player games with near-optimal time and sample complexity,” (A Sidford, M Wang, L Yang, Y Ye), International Conference on Artificial Intelligence and Statistics, 2992-3002, 2020.
• [C76] “Distributionally Robust Local Non-parametric Conditional Estimation,” (VA Nguyen, F Zhang, J Blanchet, E Delage, Y Ye), Advances in Neural Information Processing Systems 2020.
• [C75] “Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices,” (JC Duchi, O Hinder, A Naber, Y Ye), Advances in Neural Information Processing Systems 2020.
• [C74] “Simple and Fast Algorithm for Binary Integer and Online Linear Programming,” (Xiaocheng Li, Chunlin Sun, Yinyu Ye), Advances in Neural Information Processing Systems 2020.
• [C73] “Adaptive Discrete Phase Retrieval,” (M Charikar, X Wu, Y Ye), Symposium on Simplicity in Algorithms, 47- 56, 2020.
• [C72] “Improved Upper and Lower Bounds for Policy and Strategy Iteration,” (Sidford, Wang, Yang and Ye), NeurIPS 2019 Workshop on Optimization Foundation for Reinforcement Learning.
• [C71] “Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity,” (Sidford, Wang, Yang and Ye), NeurIPS 2019 Workshop on Optimization Foundation for Reinforcement Learning.
• [C70] “Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem,” (Ge, Wang, Xiong, and Ye), NeurIPS 2019.
• [C69] “Advances in Inverse Planning Algorithm and Strategy,” (Masoud Zarepisheh, Baris Ungun, Ruijiang Li, Yinyu Ye, Stephen Boyd, Lei Xing), in Advanced and Emerging Technologies in Radiation Oncology Physics, pp. 169-198 Publisher CRC Press, 2018/5/24.
• [C68] “Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model,” (Sidford, Wang, Wu, Yang and Ye), NIPS 2018.
• [C67] “Learning in Games with Lossy Feedback,” (Zhou, Mertikopoulos, Athey, Bambos, Glynn, Ye) NIPS 2018.
• [C66] “Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?” Zhengyuan Zhou, Mertikopoulos, Bambos, Glynn, Ye, Li, Li) ICML 2018 – 35th International Conference on Machine Learning, Jul 2018, Stockholm, Sweden. pp.1-10.
• [C65] “Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes,” (Sidford, Wang, Wu and Ye), SODA 2018. 14
• [C64] “Low-rank semidefinite programming: Theory and applications,” (Alex Lemon, Anthony Man-Cho So, Yinyu Ye), Foundations and Trends® in Optimization 2(1-2), 2016/8/4, pp 1-156.
• [C62] “On the Expected Convergence of Randomly Permuted ADMM,” (Sun, LUO and Ye) OPT2015 (Optimization@NIPS for Machine Learning).
• [C61] “A Mathematical Formulation for Optimal Load Shifting of Electricity Demand,” (Hu, Skorupski, Entriken and Ye), The Grid of the Future Symposium, The International Council on Large Electric Systems (CIGRE), 2014, Houston.
• [C60] “Beyond Convex Relaxation: A Polynomial–Time Non–Convex Optimization Approach to Network Localization,” (Ji, Sze, Zhou, So, and Ye), INFOCOM 2013.
• [C59] “The simplex method is strongly polynomial for deterministic Markov decision processes,” (Ian Post and Ye), SODA 2013.
• [C58] “Conditions for Correct Sensor Network Localization Using SDP Relaxation,” (Davood Shamsi, Nicole Taheri, Zhisu Zhu, Yinyu Ye), Discrete Geometry and Optimization, Pages 279-301, Springer International Publishing, 2013..
• [C57] “Reservoir Management Using Two-Stage Optimization with Streamline Simulation,” (T. Wen, M.R. Thiele, D. Echeverria Ciaurri, K. Aziz, and Y. Ye), the 13th European Conference on the Mathematics of Oil Recovery, 10 – 13 September 2012, Biarritz, France.
• [C56] “An Optimization Approach to Improving Collections of Shape Maps,” (Nguyen, Mirela Ben-Chen, Katarzyna Welnicka, Ye, and Guibas), Eurographics Symposium on Geometry Processing 2011, Computer Graphics Forum, Volume 30:5 (2011).
• [C55] “Fast and Near-Optimal Matrix Completion via Randomized Basis Pursuit” (Zhu, So and Ye), in AMS/IP Studies in Advanced Mathematics, Volume 51(2), 2011.
• [C54] “An Optimization Approach to Improving Collections of Shape Maps,” (Nguyen, Mirela Ben-Chen, Katarzyna Welnicka, Ye, and Guibas), Eurographics Symposium on Geometry Processing 2011, Computer Graphics Forum, Volume 30:5 (2011).
• [C51] “The Complexity of Determining the Uniqueness of Tarski’s Fixed Point under the Lexicographic Ordering,” (Dang and Ye), Proc. of the WINE 2010.
• [C50] “Correlation Robust Stochastic Optimization,” (Agrawal, Ding, Saberi and Ye), Proc. of the SODA 2010.
• [C49] “Universal Rigidity: Towards Accurate and Efficient Localization of Wireless Networks” (Zhu, So and Ye), Proc. of the INFOCOM 2010.
• [C48] “A Unified Framework for Dynamic Pari-Mutuel Information Market Design,” (Agrawal, Delage, Peters, Wang, and Ye), Proc. of the EC2009.
• [C47] “A FPTAS for Computing a Symmetric Leontief Competitive Economy Equilibrium,” (Zhu, Dang and Ye), Proc. of the WINE 2008.
• [C46] “Parimutuel Betting on Permutations,” (Agrawal, Wang and Ye), Proc. of WINE 2008.
• [C40] “Pari-mutuel Markets: Mechanisms and Performance” (Peters, So and Ye), Proc. of WINE 2007.
• [C39] “A Note on Equilibrium Pricing as Convex Optimization” (Chen, Ye, Zhang), Proc. of WINE 2007.
• [C38] “Stochastic Combinatorial Optimization with Controllable Risk Aversion Level,” (So, Zhang and Ye), Proc. of the 9th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2006), LNCS 4110, pp. 224-235, 2006.
• [C37] “A Semidefinite Programming Approach to Tensegrity Theory and Realizability of Graphs” (So and Ye), Proc. of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 766-775, 2006.
• [C36] “Leontief Economies Encode Nonzero Sum Two-Player Games” (Codenotti, Saberi, Varadarajan and Ye), Proc. of the SODA 2006.
• [C35] “On Solving Coverage Problems in a Wireless Sensor Network Using Voronoi Diagrams,” (So and Ye), Proc. of the WINE 2005.
• [C34] “On Exchange Market Equilibria with Leontief’s Utility: Freedom of Pricing Leads to Rationality,” Proc. of the WINE 2005.
• [C33] “Integration of Angle of Arrival Information for Multimodal Sensor Network Localization using Semidefinite Programming,” (Biswas, Aghajan and Ye), Proc. of the 39th Asilomar Conference on Signals, Systems and Computers, 2005.
• [C32] “On Approximating Complex Quadratic Optimization Problems via Semidefinite Programming Relaxations is available,” (So, Zhang and Ye), Proc. of the IPCO 2005.
• [C31] “Market Equilibria for Homothetic, Quasi-Concave Utilities and Economies of Scale in Production,” (Jain, Vazirani and Ye), SODA ’05 Proc. of the sixteenth annual ACM-SIAM symposium on Discrete algorithms.
• [C30] “Theory of Semidefinite Programming for Sensor Network Localization,” (So and Ye), Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms (SODA), 2005, pp. 405-414.
• [C29] “Semidefinite Programming for Ad Hoc Wireless Sensor Network Localization” (Biswas and Ye), Proc. of the 3rd international symposium on Information processing in sensor networks, Berkeley, 2004, pp 46-54.
• [C28] “Optimal arrival traffic spacing via dynamic programming,” (Bayen, Tomlin, Callantine, Ye and Zhang), AIAA Conference on Guidance, Navigation and Control, AIAA Paper 2004-5228, Aug. 2004.
• [C27] “An approximation algorithm for scheduling aircraft with holding time,” (Bayen, Tomlin, Ye and Zhang), Proc. of the 43rd IEEE Conference on Decision and Control, pp. 2760-2767, Dec. 2004.
• [C26] “MILP Formulation and Polynomial Time Algorithm for an Aircraft Scheduling Problem,” (Bayen, Tomlin, Ye, and Zhang), Proc. of the 42th IEEE Conference on Decision and Control (CDC), pp 5003-5010, 2003.
• [C25] “An Improved Algorithm for Approximating the Radii of Point Sets,” (Ye and Zhang), Proc. of the 6th International Workshop on Approximation Algorithms for Combinatorial Optimization (APPROX) LNCS 2764, pp. 178-187, 2003.
• [C24] “A 2-approximation algorithm for the soft-capacitated facility location problem,” (Mahdian , Ye and Zhang), Proc. of the 6th International Workshop on Approximation Algorithms for Combinatorial Optimization (APPROX), LNCS 2764, pp. 129-140, 2003.
• [C23] “Improved Combinatorial Approximation Algorithms for the k-Level Facility Location Problem,” (Ageev, Ye and Zhang), Proc. of the 30th International Colloquium on Automata, Languages and Programming (ICALP), LNCS 2719, pp. 145-156, 2003.
• [C22] “A 1.52-approximation algorithm for the uncapacitated facility location problem,” (Mahdian, Ye and Zhang), in Klaus Jansen, Stefano Leonardi and Vijay V. Vazirani (Eds.), Proc. of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization, APPROX 2002 (Rome, Italy, September 17-21, 2002). Also see “Improved Approximation Algorithms for the Metric Facility Location Problem,” Lecture Notes in Computer Science 2462, Springer, 229-242 and SIAM J Computing 36(2) (2007) 411-432.
• [C13] “An accelerated interior-point method whose running time depends only on A,” (Vavasis and Ye), Proc. of the Twenty-Sixth ACM Symposium on Theory of Computing (1994) 512-521.
• [C8] “On the Q-order of convergence of interior-point algorithms for linear programming,” in Wu Fang, ed., Proc. of the 1992 Symp on Applied Mathematics (Institute of Applied Mathematics, Chinese Academy of Sciences, 1992).
• [C3] “Interior point algorithms for quadratic programming problems” (Pardalos, Ye and Han), Proc. of the Conf. on Optimization Methods and their Applications (in Russian), Nauka, USSR (1990), pp. 194-213. Other contributions including significant technical reports and book chapters
• [C53] “A Distributed Method for Solving Semidefinite Programs Arising from Ad Hoc Wireless Sensor Network Localization,” (Biswas and Ye), Multiscale Optimization Methods and Applications, Nonconvex Optimization and Its Applications, 2006, Volume 82, 69-84.
• [C52] “Probabilistic analysis of semidefinite relaxation detectors in multiple-input, multiple-output systems,” (So and Ye), in Convex Optimization in Signal Processing and Communications, Chapter 5, D. P. Palomar and Y. C. Eldar, Ed., Cambridge University Press, 2010.
• [C45] “Solving Min-Max Multi-Depot Vehicle Routing Problem,” (Carlsson, Ge, Subramaniam, Wu and Ye), in FIELDS Book on Global Optimization, 2008.
• [C43] “On Analyzing Semidefinite Programming Relaxations of Complex Quadratic Optimization Problems,” (So, Ye, and Zhang), 8-1, Handbook of Approximation Algorithms and Metaheuristics, ed. Teofilo F. Gonzalez, Chapman & Hall/CRC, 2007.
• [C42] “Greedy Algorithms for Metric Facility Location Problems,” (So, Ye, and Zhang), 39-1, Handbook of Approximation Algorithms and Metaheuristics, ed. Teofilo F. Gonzalez, Chapman & Hall/CRC, 2007.
• [C41] “Semidefinite Programming for Sensor Network and Graph Localization,” (So and Ye), in `Robust Optimization-Directed Design’ (ed. A.J. Kurdila, P.M. Pardalos, M. Zabarankin), pp. 127-143, Springer, 2006.
• [C21] “Semidefinite Programs,” in A. Kent and J. Williams eds., Encyclopedia of Computer Science and Technology, 44:29 (Marcel Dekker, 2001) 247-361.
• [C20] “Semidefinite Programming Relaxations for Nonconvex Quadratic Optimization,” (Nesterov, Wolkowics, and Ye), in Handbook on Semidefinite Programming (Kluwer, Boston, 2000} 361-419.
• [C19] “Approximating Maximum Stable Set and Minimum Graph Coloring Problems with the Positive Semidefinite Relaxation,” (Benson and Ye), in M. Ferris and J. Pang eds., Applications and Algorithms of Complementarity (Kluwer Academic Publishers, 2000) 1-18.
• [C18] “Application of Semidefinite Programming to Circuit Partitioning,” (C. Choi and Y. Ye), in P. Pardalos eds., Approximation and Complexity in Numerical Optimization (Kluwer Academics Publishers, 2000) 130-136.
• [C17] “A simplification to ‘a primal-dual interior point method whose running time depends only on the constraint matrix’,” (Vavasis and Ye), in S. Zhang et al, eds., High Performance Optimization, Applied Optimization 33 (Kluwer Academic Publication, 2000) pp. 233-243.
• [C16] “Semidefinite relaxations, multivariate normal distributions, and order statistics,” (Bertsimas and Ye), Handbook of Combinatorial Optimization (Vol. 3), D.-Z. Du and P.M. Pardalos (Eds.) pp. 1-19, (1998 Kluwer Academic Publishers).
• [C14] “On a Homogeneous Algorithm for a Monotone Complementarity Problem with Nonlinear Equality Constraints,” (Andersen and Ye), in Michael C. Ferris and Jong-Shi Pang, eds., Complementarity and variational Problems: State of the art (SIAM, 1997) pp. 1-11.
• [C12] “A genuine quadratically convergent polynomial interior point algorithm for linear programming,” (Luo and Ye), in Ding-Zhu Du and Jie Sun, eds., Advances in Optimization and Approximation (Kluwer Academic Publishers, Boston, 1994).
• [C11] “On the complexity of a column generation algorithm for convex or quasiconvex feasibility problems,” (Goffin, Luo and Ye), in W. Hager, D. Hearn and P. Pardalos eds., Large Scale Optimization: State of the Art (Kluwer Academic Publishers, Boston, 1994) pp. 182-191.
• [C10] “Average performance of a self-dual interior-point algorithm for linear programming,” (Anstreicher, Ji, Potra and Ye), in P. Pardalos eds., Complexity in Numerical Optimization (World Scientific, New Jersey, 1993) pp. 1-15.
• [C9] “Translation cuts for convex minimization,” (Burke, Goldstein, Tseng and Ye), in P. Pardalos eds., Complexity in Numerical Optimization (World Scientific, New Jersey, 1993) pp. 57-73.
• [C7] “A further result on potential reduction algorithm for the P-matrix linear complementarity problem,” in P. Pardalos eds., Advances in Optimization and Parallel Computing (North-Holland, NY, 1992) pp. 310-316.
• [C6] “A new complexity result on minimization of a quadratic function with a sphere constraint,” in C. Floudas and P. Pardalos eds., Recent Advances in Global Optimization (Princeton University Press, NJ, 1992).
• [C5] “Interior-point algorithms for solving nonlinear optimization problems,” (Han, Pardalos and Ye), COAL Newsletter 19 (1991) 45-54.
• [C4] “Interior-point algorithms for quadratic programming,” in S. Kumar ed., Recent Developments in Mathematical Programming (Gordon & Breach Scientific Publishers, Philadelphia, 1991).
• [C2] “Computational aspects of an interior point algorithm for quadratic programming problems with box constraints,” (Han, Pardalos and Ye), in T. F. Coleman and Y. Li eds., Large-Scale Numerical Optimization (SIAM, Philadelphia, 1990) 92-112.
• [C1] “An extension of Karmarkar’s algorithm and the trust region method for quadratic programming,” in Progress in Mathematical Programming (N. Megiddo ed.), Springer Verlag, New York (1989) 49-63.

Working Papers

• [W19] “Computations and Complexities of Tarski’s Fixed Points and Supermodular Games,” (C Dang, Q Qi, Y Ye), 2021, arXiv preprint arXiv:2005.09836.
• [W18] “Fisher Markets with Linear Constraints: Equilibrium Properties and Efficient Distributed Algorithms, (Devansh Jalota, Marco Pavone, Qi Qi, Yinyu Ye), 2021, arXiv preprint arXiv:2106.10412.
• [W17] “Computations and Complexities of Tarski’s Fixed Points and Supermodular Games,” (C Dang, Q Qi, Y Ye), arXiv preprint arXiv:2005.09836, 2020.
• [W16] “Sequential batch learning in finite-action linear contextual bandits,” (Y Han, Z Zhou, Z Zhou, J Blanchet, PW Glynn, Y Ye), arXiv preprint arXiv:2004.06321, 2020.
• [W15] “High-Dimensional Learning under Approximate Sparsity: A Unifying Framework for Nonsmooth Learning and Regularized Neural Networks,” (Hongcheng Liu, Yinyu Ye), arXiv preprint arXiv:1903.00616, 2019.
• [W14] “A Robust Approach for Renewable Energy Exchange with a Fleet of Plug-In Electric Vehicles,” (Nicole Taheri, Robert Entriken, and Yinyu Ye), Working Paper, October 2015.
• [W13] “Online Allocation Rules in Display Advertising,” (Shamsi, Holtan, Luenberger, Ye), Working Paper, June 2014.
• [W12] “Sparse Portfolio Selection via Quasi-Norm Regularization,” (Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, Yinyu Ye), Working Paper, December 2013.
• [W11] “Computational Models and Complexities of Tarski’s Fixed Points,” (Dang, Qi and Ye), Working Paper, September, 2011
• [W10] “Existence of Positive Steady States for Mass Conserving and Mass-Action Chemical Reaction Networks with a Single Terminal-Linkage Class,” (Santiago Akle, Onkar Dalal, Ronan M. T. Fleming, Michael Saunders, Nicole Taheri, Yinyu Ye), May 2011.
• [W9] “NP-Hardness Results Related to PPAD” (Dang and Ye), Working Paper, January 2010.
• [W8] “Solving sparse semidefinite programs using the dual scaling algorithm with an iterative solver,” (Choi and Ye), Working Paper, Department of Management Sciences, University of Iowa, March 2000.
• [W7] “Computational Optimization Laboratory Positive Semidefinite Programming User Guide,” (Benson, Ye, and Zhang), Working Paper, Department of Management Sciences, University of Iowa, February 1999.
• [W6] “Convergence behavior of the central path for homogeneous and self-dual cones,” Working Note, Department of Management Sciences, The University of Iowa, December, 1995.
• [W5] “A low complexity combined phase I-phase II potential reduction algorithm for linear programming,” Working Paper No. 91-1, College of Business Administration, University of Iowa, 1991.
• [W4] “Line search in potential reduction algorithms for linear programming,” Working Paper, College of Business Administration, University of Iowa, 1989.
• [W3] “A `build-up’ interior method for linear programming,” (Dantzig and Ye) SOL Report, Department of Operations Research, Stanford University, 1990.
• [W2] “Bimatrix equilibrium points and potential functions,” Working Paper No. 88-16, College of Business Administration, University of Iowa, 1988.
• [W1] “Further development of the interior algorithm for convex quadratic programming,” manuscript, Stanford University and Integrated Systems Inc., Stanford, 1987.