Calculation of Pseudospectra by the Arnoldi Iteration. - Kim-Chuan Toh; Lloyd N. Trefethen (SIAM J. Sci. Comput., 1996)

GMRES vs. Ideal GMRES. - Kim-Chuan Toh (SIAM J. Matrix Anal. Appl., 1997)

On the Nesterov-Todd Direction in Semidefinite Programming. - Michael J. Todd; Kim-Chuan Toh; Reha H. Tütüncü (SIAM J. Optim., 1998)

The Chebyshev Polynomials of a Matrix. - Kim-Chuan Toh; Lloyd N. Trefethen (SIAM J. Matrix Anal. Appl., 1998)

From Potential Theory to Matrix Iterations in Six Steps. - Tobin A. Driscoll; Kim-Chuan Toh; Lloyd N. Trefethen (SIAM Rev., 1998)

Primal-Dual Path-Following Algorithms for Determinant Maximization Problems With Linear Matrix Inequalities. - Kim-Chuan Toh (Comput. Optim. Appl., 1999)

The Kreiss Matrix Theorem on a General Complex Domain. - Kim-Chuan Toh; Lloyd N. Trefethen (SIAM J. Matrix Anal. Appl., 1999)

Some New Search Directions for Primal-Dual Interior Point Methods in Semidefinite Programming. - Kim-Chuan Toh (SIAM J. Optim., 2000)

Computing the Sobolev Regularity of Refinable Functions by the Arnoldi Method. - Amos Ron; Zuowei Shen; Kim-Chuan Toh (SIAM J. Matrix Anal. Appl., 2001)

A Note on the Calculation of Step-Lengths in Interior-Point Methods for Semidefinite Programming. - Kim-Chuan Toh (Comput. Optim. Appl., 2002)

An Analytic Center Cutting Plane Method for Semidefinite Feasibility Problems. - Jie Sun 001; Kim-Chuan Toh; Gongyun Zhao (Math. Oper. Res., 2002)

Solving Some Large Scale Semidefinite Programs via the Conjugate Residual Method. - Kim-Chuan Toh; Masakazu Kojima (SIAM J. Optim., 2002)

A Multiple-Cut Analytic Center Cutting Plane Method for Semidefinite Feasibility Problems. - Kim-Chuan Toh; Gongyun Zhao; Jie Sun 001 (SIAM J. Optim., 2002)

Solving semidefinite-quadratic-linear programs using SDPT3. - Reha H. Tütüncü; Kim-Chuan Toh; Michael J. Todd (Math. Program., 2003)

Convergence Analysis of an Infeasible Interior Point Algorithm Based on a Regularized Central Path for Linear Complementarity Problems. - Guanglu Zhou; Kim-Chuan Toh; Gongyun Zhao (Comput. Optim. Appl., 2004)

Polynomiality of an inexact infeasible interior point algorithm for semidefinite programming. - Guanglu Zhou; Kim-Chuan Toh (Math. Program., 2004)

Solving Large Scale Semidefinite Programs via an Iterative Solver on the Augmented Systems. - Kim-Chuan Toh (SIAM J. Optim., 2004)

Efficient Algorithms for the Smallest Enclosing Ball Problem. - Guanglu Zhou; Kim-Chuan Toh; Jie Sun 001 (Comput. Optim. Appl., 2005)

Computation of condition numbers for linear programming problems using Peña's method. - Joo-Siong Chai; Kim-Chuan Toh (Optim. Methods Softw., 2006)

Solving Second Order Cone Programming via a Reduced Augmented System Approach. - Zhi Cai; Kim-Chuan Toh (SIAM J. Optim., 2006)

Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements. - Pratik Biswas; Tzu-Chen Liang; Kim-Chuan Toh; Yinyu Ye 001; Ta-Chung Wang (IEEE Trans Autom. Sci. Eng., 2006)

Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming. - Joo-Siong Chai; Kim-Chuan Toh (Comput. Optim. Appl., 2007)

Behavioral measures and their correlation with IPM iteration counts on semi-definite programming problems. - Robert M. Freund; Fernando Ordóñez; Kim-Chuan Toh (Math. Program., 2007)

An inexact primal-dual path following algorithm for convex quadratic SDP. - Kim-Chuan Toh (Math. Program., 2008)

A Distributed SDP Approach for Large-Scale Noisy Anchor-Free Graph Realization with Applications to Molecular Conformation. - Pratik Biswas; Kim-Chuan Toh; Yinyu Ye 001 (SIAM J. Sci. Comput., 2008)

An SDP-Based Divide-and-Conquer Algorithm for Large-Scale Noisy Anchor-Free Graph Realization. - Ngai-Hang Z. Leung; Kim-Chuan Toh (SIAM J. Sci. Comput., 2009)

On the implementation of a log-barrier progressive hedging method for multistage stochastic programs. - Xinwei Liu; Kim-Chuan Toh; Gongyun Zhao (J. Comput. Appl. Math., 2010)

An inexact interior point method for L 1-regularized sparse covariance selection. - Lu Li; Kim-Chuan Toh (Math. Program. Comput., 2010)

Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm. - Chengjing Wang; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2010)

A Newton-CG Augmented Lagrangian Method for Semidefinite Programming. - Xin-Yuan Zhao; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2010)

A coordinate gradient descent method for ℓ1-regularized convex minimization. - Sangwoon Yun; Kim-Chuan Toh (Comput. Optim. Appl., 2011)

A block coordinate gradient descent method for regularized convex separable optimization and covariance selection. - Sangwoon Yun; Paul Tseng; Kim-Chuan Toh (Math. Program., 2011)

An Accelerated Proximal Gradient Algorithm for Frame-Based Image Restoration via the Balanced Approach. - Zuowei Shen; Kim-Chuan Toh; Sangwoon Yun (SIAM J. Imaging Sci., 2011)

An implementable proximal point algorithmic framework for nuclear norm minimization. - Yong-Jin Liu 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2012)

An Inexact Accelerated Proximal Gradient Method for Large Scale Linearly Constrained Convex SDP. - Kaifeng Jiang; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2012)

Using a Distributed SDP Approach to Solve Simulated Protein Molecular Conformation Problems. - Xingyuan Fang; Kim-Chuan Toh (Distance Geometry, 2013)

Inference of Spatial Organizations of Chromosomes Using Semi-definite Embedding Approach and Hi-C Data. - ZhiZhuo Zhang; Guoliang Li 002; Kim-Chuan Toh; Wing-Kin Sung (RECOMB, 2013)

3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data. - ZhiZhuo Zhang; Guoliang Li 002; Kim-Chuan Toh; Wing-Kin Sung (J. Comput. Biol., 2013)

A Proximal Point Algorithm for Log-Determinant Optimization with Group Lasso Regularization. - Junfeng Yang; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2013)

A Proximal Point Algorithm for Sequential Feature Extraction Applications. - Xuan Vinh Doan; Kim-Chuan Toh; Stephen A. Vavasis (SIAM J. Sci. Comput., 2013)

A Probabilistic Model for Minmax Regret in Combinatorial Optimization. - Karthik Natarajan; Dongjian Shi; Kim-Chuan Toh (Oper. Res., 2014)

Image Restoration with Mixed or Unknown Noises. - Zheng Gong; Zuowei Shen; Kim-Chuan Toh (Multiscale Model. Simul., 2014)

An introduction to a class of matrix cone programming. - Chao Ding; Defeng Sun; Kim-Chuan Toh (Math. Program., 2014)

A partial proximal point algorithm for nuclear norm regularized matrix least squares problems. - Kaifeng Jiang; Defeng Sun; Kim-Chuan Toh (Math. Program. Comput., 2014)

On the Moreau-Yosida Regularization of the Vector k-Norm Related Functions. - Bin Wu; Chao Ding; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2014)

A Convergent 3-Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block. - Min Li; Defeng Sun; Kim-Chuan Toh (Asia Pac. J. Oper. Res., 2015)

Semi-definite programming relaxation of quadratic assignment problems based on nonredundant matrix splitting. - Jiming Peng; Tao Zhu; Hezhi Luo; Kim-Chuan Toh (Comput. Optim. Appl., 2015)

Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization. - Yu-Xiang Wang 003; Choon Meng Lee; Loong-Fah Cheong; Kim-Chuan Toh (Int. J. Comput. Vis., 2015)

SDPNAL (+ ) : a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints. - Liuqin Yang; Defeng Sun; Kim-Chuan Toh (Math. Program. Comput., 2015)

A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints. - Defeng Sun; Kim-Chuan Toh; Liuqin Yang (SIAM J. Optim., 2015)

Simultaneous Clustering and Model Selection for Tensor Affinities. - Zhuwen Li; Shuoguang Yang; Loong-Fah Cheong; Kim-Chuan Toh (CVPR, 2016)

On the Convergence Properties of a Majorized Alternating Direction Method of Multipliers for Linearly Constrained Convex Optimization Problems with Coupled Objective Functions. - Ying Cui 004; Xudong Li 004; Defeng Sun; Kim-Chuan Toh (J. Optim. Theory Appl., 2016)

A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems. - Caihua Chen; Yong-Jin Liu 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2016)

A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems. - Sunyoung Kim; Masakazu Kojima; Kim-Chuan Toh (Math. Program., 2016)

A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2016)

A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization. - Min Li; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2016)

An Efficient Inexact ABCD Method for Least Squares Semidefinite Programming. - Defeng Sun; Kim-Chuan Toh; Liuqin Yang (SIAM J. Optim., 2016)

A robust Lagrangian-DNN method for a class of quadratic optimization problems. - Naohiko Arima; Sunyoung Kim; Masakazu Kojima; Kim-Chuan Toh (Comput. Optim. Appl., 2017)

A note on the convergence of ADMM for linearly constrained convex optimization problems. - Liang Chen 006; Defeng Sun; Kim-Chuan Toh (Comput. Optim. Appl., 2017)

A bounded degree SOS hierarchy for polynomial optimization. - Jean B. Lasserre; Kim-Chuan Toh; Shouguang Yang (EURO J. Comput. Optim., 2017)

A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification. - Naoki Ito; Akiko Takeda; Kim-Chuan Toh (J. Mach. Learn. Res., 2017)

An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming. - Liang Chen 006; Defeng Sun; Kim-Chuan Toh (Math. Program., 2017)

An Efficient Semismooth Newton Based Algorithm for Convex Clustering. - Yancheng Yuan; Defeng Sun; Kim-Chuan Toh (ICML, 2018)

Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems. - Naoki Ito; Sunyoung Kim; Masakazu Kojima; Akiko Takeda; Kim-Chuan Toh (J. Glob. Optim., 2018)

Spectral operators of matrices. - Chao Ding; Defeng Sun; Jie Sun 001; Kim-Chuan Toh (Math. Program., 2018)

Max-norm optimization for robust matrix recovery. - Ethan X. Fang; Han Liu 001; Kim-Chuan Toh; Wen-Xin Zhou (Math. Program., 2018)

QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (Math. Program. Comput., 2018)

Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity. - Tillmann Weißer; Jean B. Lasserre; Kim-Chuan Toh (Math. Program. Comput., 2018)

Simultaneous Clustering and Model Selection: Algorithm, Theory and Applications. - Zhuwen Li; Loong-Fah Cheong; Shuoguang Yang; Kim-Chuan Toh (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2018)

On Efficiently Solving the Subproblems of a Level-Set Method for Fused Lasso Problems. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2018)

Bounds for Random Binary Quadratic Programs. - Karthik Natarajan; Dongjian Shi; Kim-Chuan Toh (SIAM J. Optim., 2018)

On the Closed-form Proximal Mapping and Efficient Algorithms for Exclusive Lasso Models. - Yancheng Yuan; Meixia Lin; Defeng Sun; Kim-Chuan Toh (CoRR, 2019)

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems. - Peipei Tang; Chengjing Wang; Defeng Sun; Kim-Chuan Toh (CoRR, 2019)

Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method. - Ziyan Luo; Defeng Sun; Kim-Chuan Toh; Naihua Xiu (J. Mach. Learn. Res., 2019)

On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming. - Ying Cui 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2019)

A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2019)

Computing the Best Approximation over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone. - Ying Cui 004; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2019)

Efficient Sparse Semismooth Newton Methods for the Clustered Lasso Problem. - Meixia Lin; Yong-Jin Liu; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2019)

Best Nonnegative Rank-One Approximations of Tensors. - Shenglong Hu; Defeng Sun; Kim-Chuan Toh (SIAM J. Matrix Anal. Appl., 2019)

Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems With Binary, Box, and Complementarity Constraints. - Naoki Ito; Sunyoung Kim; Masakazu Kojima; Akiko Takeda; Kim-Chuan Toh (ACM Trans. Math. Softw., 2019)

Doubly nonnegative relaxations are equivalent to completely positive reformulations of quadratic optimization problems with block-clique graph structures. - Sunyoung Kim; Masakazu Kojima; Kim-Chuan Toh (J. Glob. Optim., 2020)

On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2020)

An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems. - Yangjing Zhang; Ning Zhang; Defeng Sun; Kim-Chuan Toh (Math. Program., 2020)

SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0). - Defeng Sun; Kim-Chuan Toh; Yancheng Yuan; Xin-Yuan Zhao (Optim. Methods Softw., 2020)

Spectral Operators of Matrices: Semismoothness and Characterizations of the Generalized Jacobian. - Chao Ding; Defeng Sun; Jie Sun 001; Kim-Chuan Toh (SIAM J. Optim., 2020)

A Geometrical Analysis on Convex Conic Reformulations of Quadratic and Polynomial Optimization Problems. - Sunyoung Kim; Masakazu Kojima; Kim-Chuan Toh (SIAM J. Optim., 2020)

An Asymptotically Superlinearly Convergent Semismooth Newton Augmented Lagrangian Method for Linear Programming. - Xudong Li 004; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2020)

A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems. - Yangjing Zhang; Ning Zhang; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2020)

Subspace quadratic regularization method for group sparse multinomial logistic regression. - Rui Wang 060; Naihua Xiu; Kim-Chuan Toh (Comput. Optim. Appl., 2021)

STRIDE along Spectrahedral Vertices for Solving Large-Scale Rank-One Semidefinite Relaxations. - Heng Yang 002; Ling Liang; Kim-Chuan Toh; Luca Carlone (CoRR, 2021)

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm. - Defeng Sun; Kim-Chuan Toh; Yancheng Yuan (J. Mach. Learn. Res., 2021)

A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters. - Lei Yang; Jia Li; Defeng Sun; Kim-Chuan Toh (J. Mach. Learn. Res., 2021)

On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming. - Liang Chen 006; Xudong Li 004; Defeng Sun; Kim-Chuan Toh (Math. Program., 2021)

A Newton-bracketing method for a simple conic optimization problem. - Sunyoung Kim; Masakazu Kojima; Kim-Chuan Toh (Optim. Methods Softw., 2021)

An Inexact Augmented Lagrangian Method for Second-Order Cone Programming with Applications. - Ling Liang; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2021)

An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems. - Ning Zhang; Yangjing Zhang; Defeng Sun; Kim-Chuan Toh (SIAM J. Math. Data Sci., 2021)

Semi-proximal Augmented Lagrangian-Based Decomposition Methods for Primal Block-Angular Convex Composite Quadratic Conic Programming Problems. - Xin Yee Lam; Defeng Sun; Kim-Chuan Toh (INFORMS J. Optim., 2021)

Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through Convex Lifting. - Ching-pei Lee; Ling Liang; Tianyun Tang; Kim-Chuan Toh (CoRR, 2022)

Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant. - Ngoc Hoang Anh Mai; Victor Magron; Jean-Bernard Lasserre; Kim-Chuan Toh (CoRR, 2022)

On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations. - Hong T. M. Chu; Kim-Chuan Toh; Yangjing Zhang (J. Mach. Learn. Res., 2022)

On Degenerate Doubly Nonnegative Projection Problems. - Ying Cui 004; Ling Liang; Defeng Sun; Kim-Chuan Toh (Math. Oper. Res., 2022)

A New Homotopy Proximal Variable-Metric Framework for Composite Convex Minimization. - Quoc Tran-Dinh; Ling Liang; Kim-Chuan Toh (Math. Oper. Res., 2022)

Doubly nonnegative relaxations for quadratic and polynomial optimization problems with binary and box constraints. - Sunyoung Kim; Masakazu Kojima; Kim-Chuan Toh (Math. Program., 2022)

An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems. - Meixia Lin; Defeng Sun; Kim-Chuan Toh (Math. Program. Comput., 2022)

Difference-of-Convex Algorithms for a Class of Sparse Group $ ell_0$ Regularized Optimization Problems. - Wenjing Li; Wei Bian; Kim-Chuan Toh (SIAM J. Optim., 2022)

Bregman Proximal Point Algorithm Revisited: A New Inexact Version and Its Inertial Variant. - Lei Yang 030; Kim-Chuan Toh (SIAM J. Optim., 2022)

QPPAL: A Two-phase Proximal Augmented Lagrangian Method for High-dimensional Convex Quadratic Programming Problems. - Ling Liang; Xudong Li 004; Defeng Sun; Kim-Chuan Toh (ACM Trans. Math. Softw., 2022)

A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering. - Yancheng Yuan; Tsung-Hui Chang; Defeng Sun; Kim-Chuan Toh (SIAM J. Optim., 2022)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning. - Kuangyu Ding; Jingyang Li; Kim-Chuan Toh (CoRR, 2023)

Convergence Guarantees for Stochastic Subgradient Methods in Nonsmooth Nonconvex Optimization. - Nachuan Xiao; Xiaoyin Hu; Kim-Chuan Toh (CoRR, 2023)

An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization. - Heng Yang 002; Ling Liang; Luca Carlone; Kim-Chuan Toh (Math. Program., 2023)

An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems. - Hong T. M. Chu; Ling Liang; Kim-Chuan Toh; Lei Yang 030 (Comput. Optim. Appl., 2023)

On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems. - Kuangyu Ding; Xin Yee Lam; Kim-Chuan Toh (Comput. Optim. Appl., 2023)

An Improved Unconstrained Approach for Bilevel Optimization. - Xiaoyin Hu; Nachuan Xiao; Xin Liu 001; Kim-Chuan Toh (SIAM J. Optim., 2023)

On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods. - Anh Duc Nguyen; Tuan Dung Nguyen; Quang Minh Nguyen; Hoang H. Nguyen; Lam M. Nguyen; Kim-Chuan Toh (AAAI, 2024)

An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. - Ling Liang; Kim-Chuan Toh; Jia-Jie Zhu (CoRR, 2024)

Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization. - Nachuan Xiao; Kuangyu Ding; Xiaoyin Hu; Kim-Chuan Toh (CoRR, 2024)

Nesterov's Accelerated Jacobi-Type Methods for Large-scale Symmetric Positive Semidefinite Linear Systems. - Ling Liang; Qiyuan Pang; Kim-Chuan Toh; Haizhao Yang (CoRR, 2024)

Vertex Exchange Method for a Class of Quadratic Programming Problems. - Ling Liang; Kim-Chuan Toh; Haizhao Yang (CoRR, 2024)

Optimization Hyper-parameter Laws for Large Language Models. - Xingyu Xie; Kuangyu Ding; Shuicheng Yan; Kim-Chuan Toh; Tianwen Wei (CoRR, 2024)

Memory-Efficient 4-bit Preconditioned Stochastic Optimization. - Jingyang Li; Kuangyu Ding; Kim-Chuan Toh; Pan Zhou 002 (CoRR, 2024)

Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training. - Pan Zhou 002; Xingyu Xie; Zhouchen Lin; Kim-Chuan Toh; Shuicheng Yan (J. Mach. Learn. Res., 2024)

Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees. - Nachuan Xiao; Xiaoyin Hu; Xin Liu 001; Kim-Chuan Toh (J. Mach. Learn. Res., 2024)

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models. - Yangjing Zhang; Ying Cui; Bodhisattva Sen; Kim-Chuan Toh (J. Mach. Learn. Res., 2024)

A Feasible Method for Solving an SDP Relaxation of the Quadratic Knapsack Problem. - Tianyun Tang; Kim-Chuan Toh (Math. Oper. Res., 2024)

Dissolving Constraints for Riemannian Optimization. - Nachuan Xiao; Xin Liu 001; Kim-Chuan Toh (Math. Oper. Res., 2024)

Self-adaptive ADMM for semi-strongly convex problems. - Tianyun Tang; Kim-Chuan Toh (Math. Program. Comput., 2024)

A highly efficient algorithm for solving exclusive lasso problems. - Meixia Lin; Yancheng Yuan; Defeng Sun; Kim-Chuan Toh (Optim. Methods Softw., 2024)

Learning graph Laplacian with MCP. - Yangjing Zhang; Kim-Chuan Toh; Defeng Sun (Optim. Methods Softw., 2024)

A Feasible Method for General Convex Low-Rank SDP Problems. - Tianyun Tang; Kim-Chuan Toh (SIAM J. Optim., 2024)

A Riemannian Dimension-Reduced Second-Order Method with Application in Sensor Network Localization. - Tianyun Tang; Kim-Chuan Toh; Nachuan Xiao; Yinyu Ye 001 (SIAM J. Sci. Comput., 2024)

Solving graph equipartition SDPs on an algebraic variety. - Tianyun Tang; Kim-Chuan Toh (Math. Program., 2024)

A Corrected Inexact Proximal Augmented Lagrangian Method with a Relative Error Criterion for a Class of Group-Quadratic Regularized Optimal Transport Problems. - Lei Yang 030; Ling Liang; Hong T. M. Chu; Kim-Chuan Toh (J. Sci. Comput., 2024)

A Sparse Smoothing Newton Method for Solving Discrete Optimal Transport Problems. - Di Hou; Ling Liang; Kim-Chuan Toh (ACM Trans. Math. Softw., 2024)

Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning. - Jingyang Li; Jiachun Pan; Vincent Y. F. Tan; Kim-Chuan Toh; Pan Zhou 002 (ICLR, 2025)

Adam-family Methods with Decoupled Weight Decay in Deep Learning. - Kuangyu Ding; Nachuan Xiao; Kim-Chuan Toh (Trans. Mach. Learn. Res., 2025)

Towards Understanding Why Data Augmentation Improves Generalization. - Jingyang Li; Jiachun Pan; Kim-Chuan Toh; Pan Zhou 002 (CoRR, 2025)

GRIFFIN: Effective Token Alignment for Faster Speculative Decoding. - Shijing Hu; Jingyang Li; Xingyu Xie; Zhihui Lu 005; Kim-Chuan Toh; Pan Zhou 002 (CoRR, 2025)

Learning-Rate-Free Momentum SGD with Reshuffling Converges in Nonsmooth Nonconvex Optimization. - Xiaoyin Hu; Nachuan Xiao; Xin Liu 001; Kim-Chuan Toh (J. Sci. Comput., 2025)

An Inexact Bregman Proximal Difference-of-Convex Algorithm with Two Types of Relative Stopping Criteria. - Lei Yang; Jingjing Hu; Kim-Chuan Toh (J. Sci. Comput., 2025)

CDOpt: a Python package for a class of Riemannian optimization. - Nachuan Xiao; Xiaoyin Hu; Xin Liu 001; Kim-Chuan Toh (Math. Program. Comput., 2025)

LoCo: Low-Bit Communication Adaptor for Large-Scale Model Training. - Xingyu Xie; Zhijie Lin 001; Kim-Chuan Toh; Pan Zhou 002 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem. - Kuangyu Ding; Kim-Chuan Toh (CoRR, 2025)

On solving a rank regularized minimization problem via equivalent factorized column-sparse regularized models. - Wenjing Li; Wei Bian; Kim-Chuan Toh (Math. Program., 2025)

Stochastic Bregman Subgradient Methods for Nonsmooth Nonconvex Optimization Problems. - Kuangyu Ding; Kim-Chuan Toh (J. Optim. Theory Appl., 2025)

An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. - Ling Liang; Zusen Xu; Kim-Chuan Toh; Jia-Jie Zhu (J. Optim. Theory Appl., 2025)

Quantifying low rank approximations of third order symmetric tensors. - Shenglong Hu; Defeng Sun; Kim-Chuan Toh (Math. Program., 2025)