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2020 – today
- 2024
[j114]Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh:
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models. J. Mach. Learn. Res. 25: 8:1-8:46 (2024)- [j113]Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh:
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees. J. Mach. Learn. Res. 25: 48:1-48:53 (2024) - [j112]Pan Zhou, Xingyu Xie, Zhouchen Lin, Kim-Chuan Toh, Shuicheng Yan:
Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training. J. Mach. Learn. Res. 25: 83:1-83:74 (2024) - [j111]Lei Yang, Ling Liang
, Hong T. M. Chu, Kim-Chuan Toh:
A Corrected Inexact Proximal Augmented Lagrangian Method with a Relative Error Criterion for a Class of Group-Quadratic Regularized Optimal Transport Problems. J. Sci. Comput. 99(3): 79 (2024) - [j110]Tianyun Tang, Kim-Chuan Toh:
A Feasible Method for Solving an SDP Relaxation of the Quadratic Knapsack Problem. Math. Oper. Res. 49(1): 19-39 (2024) - [j109]Nachuan Xiao, Xin Liu, Kim-Chuan Toh:
Dissolving Constraints for Riemannian Optimization. Math. Oper. Res. 49(1): 366-397 (2024) - [j108]Tianyun Tang, Kim-Chuan Toh:
Solving graph equipartition SDPs on an algebraic variety. Math. Program. 204(1): 299-347 (2024) - [j107]Tianyun Tang, Kim-Chuan Toh:
Self-adaptive ADMM for semi-strongly convex problems. Math. Program. Comput. 16(1): 113-150 (2024) - [j106]Tianyun Tang, Kim-Chuan Toh:
A Feasible Method for General Convex Low-Rank SDP Problems. SIAM J. Optim. 34(3): 2169-2200 (2024) - [j105]Tianyun Tang, Kim-Chuan Toh, Nachuan Xiao, Yinyu Ye:
A Riemannian Dimension-Reduced Second-Order Method with Application in Sensor Network Localization. SIAM J. Sci. Comput. 46(3): 2025- (2024) - [c4]Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, Kim-Chuan Toh:
On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. AAAI 2024: 8090-8098 - [i21]Ling Liang, Kim-Chuan Toh, Jia-Jie Zhu:
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. CoRR abs/2402.06033 (2024) - [i20]Nachuan Xiao, Kuangyu Ding, Xiaoyin Hu, Kim-Chuan Toh:
Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization. CoRR abs/2404.09438 (2024) - [i19]Ling Liang, Qiyuan Pang, Kim-Chuan Toh, Haizhao Yang:
Nesterov's Accelerated Jacobi-Type Methods for Large-scale Symmetric Positive Semidefinite Linear Systems. CoRR abs/2407.03272 (2024) - [i18]Ling Liang, Kim-Chuan Toh, Haizhao Yang:
Vertex Exchange Method for a Class of Quadratic Programming Problems. CoRR abs/2407.03294 (2024) - [i17]Xingyu Xie, Zhijie Lin, Kim-Chuan Toh, Pan Zhou:
LoCo: Low-Bit Communication Adaptor for Large-scale Model Training. CoRR abs/2407.04480 (2024) - [i16]Xingyu Xie, Kuangyu Ding, Shuicheng Yan, Kim-Chuan Toh, Tianwen Wei:
Optimization Hyper-parameter Laws for Large Language Models. CoRR abs/2409.04777 (2024) - 2023
- [j104]Hong T. M. Chu, Ling Liang, Kim-Chuan Toh, Lei Yang:
An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems. Comput. Optim. Appl. 85(1): 107-146 (2023) - [j103]Kuangyu Ding, Xin Yee Lam, Kim-Chuan Toh:
On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems. Comput. Optim. Appl. 86(1): 117-161 (2023) - [j102]Heng Yang, Ling Liang, Luca Carlone, Kim-Chuan Toh:
An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization. Math. Program. 201(1): 409-472 (2023) - [j101]Xiaoyin Hu, Nachuan Xiao, Xin Liu, Kim-Chuan Toh:
An Improved Unconstrained Approach for Bilevel Optimization. SIAM J. Optim. 33(4): 2801-2829 (2023) - [i15]Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh:
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees. CoRR abs/2305.03938 (2023) - [i14]Kuangyu Ding, Jingyang Li, Kim-Chuan Toh:
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning. CoRR abs/2306.14522 (2023) - [i13]Nachuan Xiao, Xiaoyin Hu, Kim-Chuan Toh:
Convergence Guarantees for Stochastic Subgradient Methods in Nonsmooth Nonconvex Optimization. CoRR abs/2307.10053 (2023) - [i12]Kuangyu Ding, Nachuan Xiao, Kim-Chuan Toh:
Adam-family Methods with Decoupled Weight Decay in Deep Learning. CoRR abs/2310.08858 (2023) - [i11]Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, Kim-Chuan Toh:
On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. CoRR abs/2312.13970 (2023) - 2022
- [j100]Hong T. M. Chu, Kim-Chuan Toh, Yangjing Zhang:
On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations. J. Mach. Learn. Res. 23: 308:1-308:39 (2022) - [j99]Quoc Tran-Dinh, Ling Liang, Kim-Chuan Toh:
A New Homotopy Proximal Variable-Metric Framework for Composite Convex Minimization. Math. Oper. Res. 47(1): 508-539 (2022) - [j98]Ying Cui, Ling Liang, Defeng Sun, Kim-Chuan Toh:
On Degenerate Doubly Nonnegative Projection Problems. Math. Oper. Res. 47(3): 2219-2239 (2022) - [j97]Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh:
Doubly nonnegative relaxations for quadratic and polynomial optimization problems with binary and box constraints. Math. Program. 193(2): 761-787 (2022) - [j96]Meixia Lin, Defeng Sun, Kim-Chuan Toh:
An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems. Math. Program. Comput. 14(2): 223-270 (2022) - [j95]Lei Yang, Kim-Chuan Toh:
Bregman Proximal Point Algorithm Revisited: A New Inexact Version and Its Inertial Variant. SIAM J. Optim. 32(3): 1523-1554 (2022) - [j94]Wenjing Li, Wei Bian, Kim-Chuan Toh:
Difference-of-Convex Algorithms for a Class of Sparse Group $\ell_0$ Regularized Optimization Problems. SIAM J. Optim. 32(3): 1614-1641 (2022) - [j93]Yancheng Yuan, Tsung-Hui Chang, Defeng Sun, Kim-Chuan Toh:
A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering. SIAM J. Optim. 32(3): 2294-2318 (2022) - [j92]Ling Liang, Xudong Li, Defeng Sun, Kim-Chuan Toh:
QPPAL: A Two-phase Proximal Augmented Lagrangian Method for High-dimensional Convex Quadratic Programming Problems. ACM Trans. Math. Softw. 48(3): 33:1-33:27 (2022) - [i10]Ching-pei Lee, Ling Liang, Tianyun Tang, Kim-Chuan Toh:
Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through Convex Lifting. CoRR abs/2204.14067 (2022) - [i9]Ngoc Hoang Anh Mai, Victor Magron, Jean-Bernard Lasserre, Kim-Chuan Toh:
Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant. CoRR abs/2209.06175 (2022) - [i8]Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh:
CDOpt: A Python Package for a Class of Riemannian Optimization. CoRR abs/2212.02698 (2022) - 2021
- [j91]Rui Wang, Naihua Xiu, Kim-Chuan Toh:
Subspace quadratic regularization method for group sparse multinomial logistic regression. Comput. Optim. Appl. 79(3): 531-559 (2021) - [j90]Xin Yee Lam, Defeng Sun, Kim-Chuan Toh:
Semi-proximal Augmented Lagrangian-Based Decomposition Methods for Primal Block-Angular Convex Composite Quadratic Conic Programming Problems. INFORMS J. Optim. 3(3): 254-277 (2021) - [j89]Defeng Sun, Kim-Chuan Toh, Yancheng Yuan:
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm. J. Mach. Learn. Res. 22: 9:1-9:32 (2021) - [j88]Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh:
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters. J. Mach. Learn. Res. 22: 21:1-21:37 (2021) - [j87]Liang Chen, Xudong Li, Defeng Sun, Kim-Chuan Toh:
On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming. Math. Program. 185(1-2): 111-161 (2021) - [j86]Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh:
A Newton-bracketing method for a simple conic optimization problem. Optim. Methods Softw. 36(2-3): 371-388 (2021) - [j85]Ling Liang, Defeng Sun, Kim-Chuan Toh:
An Inexact Augmented Lagrangian Method for Second-Order Cone Programming with Applications. SIAM J. Optim. 31(3): 1748-1773 (2021) - [j84]Ning Zhang, Yangjing Zhang, Defeng Sun, Kim-Chuan Toh:
An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems. SIAM J. Math. Data Sci. 3(2): 524-543 (2021) - [i7]Heng Yang, Ling Liang, Kim-Chuan Toh, Luca Carlone:
STRIDE along Spectrahedral Vertices for Solving Large-Scale Rank-One Semidefinite Relaxations. CoRR abs/2105.14033 (2021) - 2020
- [j83]Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh:
Doubly nonnegative relaxations are equivalent to completely positive reformulations of quadratic optimization problems with block-clique graph structures. J. Glob. Optim. 77(3): 513-541 (2020) - [j82]Yangjing Zhang, Ning Zhang, Defeng Sun, Kim-Chuan Toh:
An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems. Math. Program. 179(1): 223-263 (2020) - [j81]Xudong Li, Defeng Sun, Kim-Chuan Toh:
On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope. Math. Program. 179(1): 419-446 (2020) - [j80]Defeng Sun, Kim-Chuan Toh, Yancheng Yuan, Xin-Yuan Zhao:
SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0). Optim. Methods Softw. 35(1): 87-115 (2020) - [j79]Chao Ding, Defeng Sun, Jie Sun, Kim-Chuan Toh:
Spectral Operators of Matrices: Semismoothness and Characterizations of the Generalized Jacobian. SIAM J. Optim. 30(1): 630-659 (2020) - [j78]Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh:
A Geometrical Analysis on Convex Conic Reformulations of Quadratic and Polynomial Optimization Problems. SIAM J. Optim. 30(2): 1251-1273 (2020) - [j77]Yangjing Zhang, Ning Zhang, Defeng Sun, Kim-Chuan Toh:
A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems. SIAM J. Optim. 30(3): 2197-2220 (2020) - [j76]Xudong Li, Defeng Sun, Kim-Chuan Toh:
An Asymptotically Superlinearly Convergent Semismooth Newton Augmented Lagrangian Method for Linear Programming. SIAM J. Optim. 30(3): 2410-2440 (2020) - [i6]Yangjing Zhang, Kim-Chuan Toh, Defeng Sun:
Learning Graph Laplacian with MCP. CoRR abs/2010.11559 (2020)
2010 – 2019
- 2019
- [j75]Ziyan Luo, Defeng Sun, Kim-Chuan Toh, Naihua Xiu:
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method. J. Mach. Learn. Res. 20: 106:1-106:25 (2019) - [j74]Xudong Li, Defeng Sun, Kim-Chuan Toh:
A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications. Math. Program. 175(1-2): 395-418 (2019) - [j73]Ying Cui, Defeng Sun, Kim-Chuan Toh:
On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming. Math. Program. 178(1-2): 381-415 (2019) - [j72]Meixia Lin, Yong-Jin Liu, Defeng Sun, Kim-Chuan Toh:
Efficient Sparse Semismooth Newton Methods for the Clustered Lasso Problem. SIAM J. Optim. 29(3): 2026-2052 (2019) - [j71]Ying Cui, Defeng Sun, Kim-Chuan Toh:
Computing the Best Approximation over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone. SIAM J. Optim. 29(4): 2785-2813 (2019) - [j70]Shenglong Hu, Defeng Sun, Kim-Chuan Toh:
Best Nonnegative Rank-One Approximations of Tensors. SIAM J. Matrix Anal. Appl. 40(4): 1527-1554 (2019) - [j69]Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh:
Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems With Binary, Box, and Complementarity Constraints. ACM Trans. Math. Softw. 45(3): 34:1-34:26 (2019) - [i5]Yancheng Yuan, Meixia Lin, Defeng Sun, Kim-Chuan Toh:
On the Closed-form Proximal Mapping and Efficient Algorithms for Exclusive Lasso Models. CoRR abs/1902.00151 (2019) - [i4]Peipei Tang, Chengjing Wang, Defeng Sun, Kim-Chuan Toh:
A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems. CoRR abs/1903.11460 (2019) - 2018
- [j68]Naoki Ito, Sunyoung Kim, Masakazu Kojima, Akiko Takeda, Kim-Chuan Toh:
Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems. J. Glob. Optim. 72(4): 619-653 (2018) - [j67]Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou:
Max-norm optimization for robust matrix recovery. Math. Program. 167(1): 5-35 (2018) - [j66]Chao Ding, Defeng Sun, Jie Sun, Kim-Chuan Toh:
Spectral operators of matrices. Math. Program. 168(1-2): 509-531 (2018) - [j65]Tillmann Weißer, Jean B. Lasserre, Kim-Chuan Toh:
Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity. Math. Program. Comput. 10(1): 1-32 (2018) - [j64]Xudong Li, Defeng Sun, Kim-Chuan Toh:
QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming. Math. Program. Comput. 10(4): 703-743 (2018) - [j63]Zhuwen Li, Loong-Fah Cheong, Shuoguang Yang, Kim-Chuan Toh:
Simultaneous Clustering and Model Selection: Algorithm, Theory and Applications. IEEE Trans. Pattern Anal. Mach. Intell. 40(8): 1964-1978 (2018) - [j62]Xudong Li, Defeng Sun, Kim-Chuan Toh:
A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems. SIAM J. Optim. 28(1): 433-458 (2018) - [j61]Karthik Natarajan, Dongjian Shi, Kim-Chuan Toh:
Bounds for Random Binary Quadratic Programs. SIAM J. Optim. 28(1): 671-692 (2018) - [j60]Xudong Li, Defeng Sun, Kim-Chuan Toh:
On Efficiently Solving the Subproblems of a Level-Set Method for Fused Lasso Problems. SIAM J. Optim. 28(2): 1842-1866 (2018) - [c3]Yancheng Yuan, Defeng Sun, Kim-Chuan Toh:
An Efficient Semismooth Newton Based Algorithm for Convex Clustering. ICML 2018: 5704-5712 - [i3]Yancheng Yuan, Defeng Sun, Kim-Chuan Toh:
An Efficient Semismooth Newton Based Algorithm for Convex Clustering. CoRR abs/1802.07091 (2018) - [i2]Defeng Sun, Kim-Chuan Toh, Yancheng Yuan:
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm. CoRR abs/1810.02677 (2018) - 2017
- [j59]Liang Chen, Defeng Sun, Kim-Chuan Toh:
A note on the convergence of ADMM for linearly constrained convex optimization problems. Comput. Optim. Appl. 66(2): 327-343 (2017) - [j58]Naohiko Arima, Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh:
A robust Lagrangian-DNN method for a class of quadratic optimization problems. Comput. Optim. Appl. 66(3): 453-479 (2017) - [j57]Jean B. Lasserre, Kim-Chuan Toh, Shouguang Yang:
A bounded degree SOS hierarchy for polynomial optimization. EURO J. Comput. Optim. 5(1-2): 87-117 (2017) - [j56]Naoki Ito, Akiko Takeda, Kim-Chuan Toh:
A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification. J. Mach. Learn. Res. 18: 16:1-16:49 (2017) - [j55]Liang Chen, Defeng Sun, Kim-Chuan Toh:
An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming. Math. Program. 161(1-2): 237-270 (2017) - 2016
- [j54]Ying Cui, Xudong Li, Defeng Sun, Kim-Chuan Toh:
On the Convergence Properties of a Majorized Alternating Direction Method of Multipliers for Linearly Constrained Convex Optimization Problems with Coupled Objective Functions. J. Optim. Theory Appl. 169(3): 1013-1041 (2016) - [j53]Xudong Li, Defeng Sun, Kim-Chuan Toh:
A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions. Math. Program. 155(1-2): 333-373 (2016) - [j52]Caihua Chen, Yong-Jin Liu, Defeng Sun, Kim-Chuan Toh:
A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems. Math. Program. 155(1-2): 435-470 (2016) - [j51]Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh:
A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems. Math. Program. 156(1-2): 161-187 (2016) - [j50]Min Li, Defeng Sun, Kim-Chuan Toh:
A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization. SIAM J. Optim. 26(2): 922-950 (2016) - [j49]Defeng Sun, Kim-Chuan Toh, Liuqin Yang:
An Efficient Inexact ABCD Method for Least Squares Semidefinite Programming. SIAM J. Optim. 26(2): 1072-1100 (2016) - [c2]Zhuwen Li, Shuoguang Yang, Loong-Fah Cheong, Kim-Chuan Toh:
Simultaneous Clustering and Model Selection for Tensor Affinities. CVPR 2016: 5347-5355 - 2015
- [j48]Min Li, Defeng Sun, Kim-Chuan Toh:
A Convergent 3-Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block. Asia Pac. J. Oper. Res. 32(4): 1550024:1-1550024:19 (2015) - [j47]Jiming Peng, Tao Zhu, Hezhi Luo, Kim-Chuan Toh:
Semi-definite programming relaxation of quadratic assignment problems based on nonredundant matrix splitting. Comput. Optim. Appl. 60(1): 171-198 (2015) - [j46]Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh:
Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization. Int. J. Comput. Vis. 111(3): 315-344 (2015) - [j45]Liuqin Yang, Defeng Sun, Kim-Chuan Toh:
SDPNAL \(+\) : a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints. Math. Program. Comput. 7(3): 331-366 (2015) - [j44]Defeng Sun, Kim-Chuan Toh, Liuqin Yang:
A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints. SIAM J. Optim. 25(2): 882-915 (2015) - 2014
- [j43]Karthik Natarajan, Dongjian Shi, Kim-Chuan Toh:
A Probabilistic Model for Minmax Regret in Combinatorial Optimization. Oper. Res. 62(1): 160-181 (2014) - [j42]Zheng Gong, Zuowei Shen, Kim-Chuan Toh:
Image Restoration with Mixed or Unknown Noises. Multiscale Model. Simul. 12(2): 458-487 (2014) - [j41]Chao Ding, Defeng Sun, Kim-Chuan Toh:
An introduction to a class of matrix cone programming. Math. Program. 144(1-2): 141-179 (2014) - [j40]Kaifeng Jiang, Defeng Sun, Kim-Chuan Toh:
A partial proximal point algorithm for nuclear norm regularized matrix least squares problems. Math. Program. Comput. 6(3): 281-325 (2014) - [j39]Bin Wu, Chao Ding, Defeng Sun, Kim-Chuan Toh:
On the Moreau-Yosida Regularization of the Vector k-Norm Related Functions. SIAM J. Optim. 24(2): 766-794 (2014) - 2013
- [j38]ZhiZhuo Zhang, Guoliang Li, Kim-Chuan Toh, Wing-Kin Sung:
3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data. J. Comput. Biol. 20(11): 831-846 (2013) - [j37]Junfeng Yang, Defeng Sun, Kim-Chuan Toh:
A Proximal Point Algorithm for Log-Determinant Optimization with Group Lasso Regularization. SIAM J. Optim. 23(2): 857-893 (2013) - [j36]Xuan Vinh Doan, Kim-Chuan Toh, Stephen A. Vavasis:
A Proximal Point Algorithm for Sequential Feature Extraction Applications. SIAM J. Sci. Comput. 35(1) (2013) - [c1]ZhiZhuo Zhang, Guoliang Li, Kim-Chuan Toh, Wing-Kin Sung:
Inference of Spatial Organizations of Chromosomes Using Semi-definite Embedding Approach and Hi-C Data. RECOMB 2013: 317-332 - [p1]Xingyuan Fang, Kim-Chuan Toh:
Using a Distributed SDP Approach to Solve Simulated Protein Molecular Conformation Problems. Distance Geometry 2013: 351-376 - [i1]Yu-Xiang Wang, Choon Meng Lee, Loong-Fah Cheong, Kim-Chuan Toh:
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization. CoRR abs/1309.1539 (2013) - 2012
- [j35]Yong-Jin Liu, Defeng Sun, Kim-Chuan Toh:
An implementable proximal point algorithmic framework for nuclear norm minimization. Math. Program. 133(1-2): 399-436 (2012) - [j34]Kaifeng Jiang, Defeng Sun, Kim-Chuan Toh:
An Inexact Accelerated Proximal Gradient Method for Large Scale Linearly Constrained Convex SDP. SIAM J. Optim. 22(3): 1042-1064 (2012) - 2011
- [j33]Sangwoon Yun, Kim-Chuan Toh:
A coordinate gradient descent method for ℓ1-regularized convex minimization. Comput. Optim. Appl. 48(2): 273-307 (2011) - [j32]Sangwoon Yun, Paul Tseng, Kim-Chuan Toh:
A block coordinate gradient descent method for regularized convex separable optimization and covariance selection. Math. Program. 129(2): 331-355 (2011) - [j31]Zuowei Shen, Kim-Chuan Toh, Sangwoon Yun:
An Accelerated Proximal Gradient Algorithm for Frame-Based Image Restoration via the Balanced Approach. SIAM J. Imaging Sci. 4(2): 573-596 (2011) - 2010
- [j30]Xinwei Liu, Kim-Chuan Toh, Gongyun Zhao:
On the implementation of a log-barrier progressive hedging method for multistage stochastic programs. J. Comput. Appl. Math. 234(2): 579-592 (2010) - [j29]Lu Li, Kim-Chuan Toh:
An inexact interior point method for L 1-regularized sparse covariance selection. Math. Program. Comput. 2(3-4): 291-315 (2010) - [j28]Xin-Yuan Zhao, Defeng Sun, Kim-Chuan Toh:
A Newton-CG Augmented Lagrangian Method for Semidefinite Programming. SIAM J. Optim. 20(4): 1737-1765 (2010) - [j27]Chengjing Wang, Defeng Sun, Kim-Chuan Toh:
Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm. SIAM J. Optim. 20(6): 2994-3013 (2010)
2000 – 2009
- 2009
- [j26]Ngai-Hang Z. Leung, Kim-Chuan Toh:
An SDP-Based Divide-and-Conquer Algorithm for Large-Scale Noisy Anchor-Free Graph Realization. SIAM J. Sci. Comput. 31(6): 4351-4372 (2009) - 2008
- [j25]Kim-Chuan Toh:
An inexact primal-dual path following algorithm for convex quadratic SDP. Math. Program. 112(1): 221-254 (2008) - [j24]Pratik Biswas, Kim-Chuan Toh, Yinyu Ye:
A Distributed SDP Approach for Large-Scale Noisy Anchor-Free Graph Realization with Applications to Molecular Conformation. SIAM J. Sci. Comput. 30(3): 1251-1277 (2008) - 2007
- [j23]Joo-Siong Chai, Kim-Chuan Toh:
Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming. Comput. Optim. Appl. 36(2-3): 221-247 (2007) - [j22]Robert M. Freund, Fernando Ordóñez, Kim-Chuan Toh:
Behavioral measures and their correlation with IPM iteration counts on semi-definite programming problems. Math. Program. 109(2-3): 445-475 (2007) - 2006
- [j21]Joo-Siong Chai, Kim-Chuan Toh:
Computation of condition numbers for linear programming problems using Peña's method. Optim. Methods Softw. 21(3): 419-443 (2006) - [j20]Zhi Cai, Kim-Chuan Toh:
Solving Second Order Cone Programming via a Reduced Augmented System Approach. SIAM J. Optim. 17(3): 711-737 (2006) - [j19]Pratik Biswas, Tzu-Chen Liang, Kim-Chuan Toh, Yinyu Ye, Ta-Chung Wang:
Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements. IEEE Trans Autom. Sci. Eng. 3(4): 360-371 (2006) - 2005
- [j18]Guanglu Zhou, Kim-Chuan Toh, Jie Sun:
Efficient Algorithms for the Smallest Enclosing Ball Problem. Comput. Optim. Appl. 30(2): 147-160 (2005) - 2004
- [j17]Guanglu Zhou, Kim-Chuan Toh, Gongyun Zhao:
Convergence Analysis of an Infeasible Interior Point Algorithm Based on a Regularized Central Path for Linear Complementarity Problems. Comput. Optim. Appl. 27(3): 269-283 (2004) - [j16]Guanglu Zhou, Kim-Chuan Toh:
Polynomiality of an inexact infeasible interior point algorithm for semidefinite programming. Math. Program. 99(2): 261-282 (2004) - [j15]Kim-Chuan Toh:
Solving Large Scale Semidefinite Programs via an Iterative Solver on the Augmented Systems. SIAM J. Optim. 14(3): 670-698 (2004) - 2003
- [j14]Reha H. Tütüncü, Kim-Chuan Toh, Michael J. Todd:
Solving semidefinite-quadratic-linear programs using SDPT3. Math. Program. 95(2): 189-217 (2003) - 2002
- [j13]Kim-Chuan Toh:
A Note on the Calculation of Step-Lengths in Interior-Point Methods for Semidefinite Programming. Comput. Optim. Appl. 21(3): 301-310 (2002) - [j12]Jie Sun, Kim-Chuan Toh, Gongyun Zhao:
An Analytic Center Cutting Plane Method for Semidefinite Feasibility Problems. Math. Oper. Res. 27(2): 332-346 (2002) - [j11]Kim-Chuan Toh, Masakazu Kojima:
Solving Some Large Scale Semidefinite Programs via the Conjugate Residual Method. SIAM J. Optim. 12(3): 669-691 (2002) - [j10]Kim-Chuan Toh, Gongyun Zhao, Jie Sun:
A Multiple-Cut Analytic Center Cutting Plane Method for Semidefinite Feasibility Problems. SIAM J. Optim. 12(4): 1126-1146 (2002) - 2001
- [j9]Amos Ron, Zuowei Shen, Kim-Chuan Toh:
Computing the Sobolev Regularity of Refinable Functions by the Arnoldi Method. SIAM J. Matrix Anal. Appl. 23(1): 57-76 (2001) - 2000
- [j8]Kim-Chuan Toh:
Some New Search Directions for Primal-Dual Interior Point Methods in Semidefinite Programming. SIAM J. Optim. 11(1): 223-242 (2000)
1990 – 1999
- 1999
- [j7]Kim-Chuan Toh:
Primal-Dual Path-Following Algorithms for Determinant Maximization Problems With Linear Matrix Inequalities. Comput. Optim. Appl. 14(3): 309-330 (1999) - [j6]Kim-Chuan Toh, Lloyd N. Trefethen:
The Kreiss Matrix Theorem on a General Complex Domain. SIAM J. Matrix Anal. Appl. 21(1): 145-165 (1999) - 1998
- [j5]Michael J. Todd, Kim-Chuan Toh, Reha H. Tütüncü:
On the Nesterov-Todd Direction in Semidefinite Programming. SIAM J. Optim. 8(3): 769-796 (1998) - [j4]Kim-Chuan Toh, Lloyd N. Trefethen:
The Chebyshev Polynomials of a Matrix. SIAM J. Matrix Anal. Appl. 20(2): 400-419 (1998) - [j3]Tobin A. Driscoll, Kim-Chuan Toh, Lloyd N. Trefethen:
From Potential Theory to Matrix Iterations in Six Steps. SIAM Rev. 40(3): 547-578 (1998) - 1997
- [j2]Kim-Chuan Toh:
GMRES vs. Ideal GMRES. SIAM J. Matrix Anal. Appl. 18(1): 30-36 (1997) - 1996
- [j1]Kim-Chuan Toh, Lloyd N. Trefethen:
Calculation of Pseudospectra by the Arnoldi Iteration. SIAM J. Sci. Comput. 17(1): 1-15 (1996)
Coauthor Index
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