Kim-Chuan Toh
Orcid: 0000-0001-7204-8933
According to our database1,
Kim-Chuan Toh
authored at least 123 papers
between 1996 and 2024.
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Bibliography
2024
Math. Program., March, 2024
An Inexact Halpern Iteration with Application to Distributionally Robust Optimization.
CoRR, 2024
On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
SIAM J. Optim., December, 2023
On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems.
Comput. Optim. Appl., September, 2023
An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems.
Comput. Optim. Appl., May, 2023
An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization.
Math. Program., 2023
Convergence Guarantees for Stochastic Subgradient Methods in Nonsmooth Nonconvex Optimization.
CoRR, 2023
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning.
CoRR, 2023
CoRR, 2023
2022
A Dimension Reduction Technique for Large-Scale Structured Sparse Optimization Problems with Application to Convex Clustering.
SIAM J. Optim., September, 2022
QPPAL: A Two-phase Proximal Augmented Lagrangian Method for High-dimensional Convex Quadratic Programming Problems.
ACM Trans. Math. Softw., 2022
Bregman Proximal Point Algorithm Revisited: A New Inexact Version and Its Inertial Variant.
SIAM J. Optim., 2022
Difference-of-Convex Algorithms for a Class of Sparse Group $\ell_0$ Regularized Optimization Problems.
SIAM J. Optim., 2022
An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems.
Math. Program. Comput., 2022
Doubly nonnegative relaxations for quadratic and polynomial optimization problems with binary and box constraints.
Math. Program., 2022
Math. Oper. Res., 2022
On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations.
J. Mach. Learn. Res., 2022
Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant.
CoRR, 2022
Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through Convex Lifting.
CoRR, 2022
2021
Semi-proximal Augmented Lagrangian-Based Decomposition Methods for Primal Block-Angular Convex Composite Quadratic Conic Programming Problems.
INFORMS J. Optim., July, 2021
An Efficient Linearly Convergent Regularized Proximal Point Algorithm for Fused Multiple Graphical Lasso Problems.
SIAM J. Math. Data Sci., 2021
An Inexact Augmented Lagrangian Method for Second-Order Cone Programming with Applications.
SIAM J. Optim., 2021
Optim. Methods Softw., 2021
On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming.
Math. Program., 2021
A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters.
J. Mach. Learn. Res., 2021
J. Mach. Learn. Res., 2021
STRIDE along Spectrahedral Vertices for Solving Large-Scale Rank-One Semidefinite Relaxations.
CoRR, 2021
Subspace quadratic regularization method for group sparse multinomial logistic regression.
Comput. Optim. Appl., 2021
2020
SIAM J. Optim., 2020
An Asymptotically Superlinearly Convergent Semismooth Newton Augmented Lagrangian Method for Linear Programming.
SIAM J. Optim., 2020
A Geometrical Analysis on Convex Conic Reformulations of Quadratic and Polynomial Optimization Problems.
SIAM J. Optim., 2020
Spectral Operators of Matrices: Semismoothness and Characterizations of the Generalized Jacobian.
SIAM J. Optim., 2020
SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0).
Optim. Methods Softw., 2020
An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems.
Math. Program., 2020
On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope.
Math. Program., 2020
Doubly nonnegative relaxations are equivalent to completely positive reformulations of quadratic optimization problems with block-clique graph structures.
J. Glob. Optim., 2020
2019
Algorithm 996: BBCPOP: A Sparse Doubly Nonnegative Relaxation of Polynomial Optimization Problems With Binary, Box, and Complementarity Constraints.
ACM Trans. Math. Softw., 2019
SIAM J. Matrix Anal. Appl., 2019
SIAM J. Optim., 2019
Computing the Best Approximation over the Intersection of a Polyhedral Set and the Doubly Nonnegative Cone.
SIAM J. Optim., 2019
A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications.
Math. Program., 2019
On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming.
Math. Program., 2019
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method.
J. Mach. Learn. Res., 2019
A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems.
CoRR, 2019
On the Closed-form Proximal Mapping and Efficient Algorithms for Exclusive Lasso Models.
CoRR, 2019
2018
On Efficiently Solving the Subproblems of a Level-Set Method for Fused Lasso Problems.
SIAM J. Optim., 2018
A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems.
SIAM J. Optim., 2018
IEEE Trans. Pattern Anal. Mach. Intell., 2018
Sparse-BSOS: a bounded degree SOS hierarchy for large scale polynomial optimization with sparsity.
Math. Program. Comput., 2018
QSDPNAL: a two-phase augmented Lagrangian method for convex quadratic semidefinite programming.
Math. Program. Comput., 2018
Equivalences and differences in conic relaxations of combinatorial quadratic optimization problems.
J. Glob. Optim., 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
2017
An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming.
Math. Program., 2017
A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification.
J. Mach. Learn. Res., 2017
EURO J. Comput. Optim., 2017
A note on the convergence of ADMM for linearly constrained convex optimization problems.
Comput. Optim. Appl., 2017
Comput. Optim. Appl., 2017
2016
SIAM J. Optim., 2016
A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization.
SIAM J. Optim., 2016
A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions.
Math. Program., 2016
A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems.
Math. Program., 2016
A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems.
Math. Program., 2016
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., 2016
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016
2015
A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints.
SIAM J. Optim., 2015
SDPNAL \(+\) : a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints.
Math. Program. Comput., 2015
Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization.
Int. J. Comput. Vis., 2015
Semi-definite programming relaxation of quadratic assignment problems based on nonredundant matrix splitting.
Comput. Optim. Appl., 2015
A Convergent 3-Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block.
Asia Pac. J. Oper. Res., 2015
2014
SIAM J. Optim., 2014
A partial proximal point algorithm for nuclear norm regularized matrix least squares problems.
Math. Program. Comput., 2014
Oper. Res., 2014
2013
SIAM J. Sci. Comput., 2013
A Proximal Point Algorithm for Log-Determinant Optimization with Group Lasso Regularization.
SIAM J. Optim., 2013
J. Comput. Biol., 2013
Inference of Spatial Organizations of Chromosomes Using Semi-definite Embedding Approach and Hi-C Data.
Proceedings of the Research in Computational Molecular Biology, 2013
Using a Distributed SDP Approach to Solve Simulated Protein Molecular Conformation Problems.
Proceedings of the Distance Geometry: Theory, Methods, and Applications, 2013
2012
An Inexact Accelerated Proximal Gradient Method for Large Scale Linearly Constrained Convex SDP.
SIAM J. Optim., 2012
Math. Program., 2012
2011
An Accelerated Proximal Gradient Algorithm for Frame-Based Image Restoration via the Balanced Approach.
SIAM J. Imaging Sci., 2011
A block coordinate gradient descent method for regularized convex separable optimization and covariance selection.
Math. Program., 2011
A coordinate gradient descent method for <i>ℓ</i><sub>1</sub>-regularized convex minimization.
Comput. Optim. Appl., 2011
2010
SIAM J. Optim., 2010
Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm.
SIAM J. Optim., 2010
An inexact interior point method for <i>L</i> <sub>1</sub>-regularized sparse covariance selection.
Math. Program. Comput., 2010
On the implementation of a log-barrier progressive hedging method for multistage stochastic programs.
J. Comput. Appl. Math., 2010
2009
An SDP-Based Divide-and-Conquer Algorithm for Large-Scale Noisy Anchor-Free Graph Realization.
SIAM J. Sci. Comput., 2009
2008
A Distributed SDP Approach for Large-Scale Noisy Anchor-Free Graph Realization with Applications to Molecular Conformation.
SIAM J. Sci. Comput., 2008
Math. Program., 2008
2007
Behavioral measures and their correlation with IPM iteration counts on semi-definite programming problems.
Math. Program., 2007
Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming.
Comput. Optim. Appl., 2007
2006
Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements.
IEEE Trans Autom. Sci. Eng., 2006
SIAM J. Optim., 2006
Computation of condition numbers for linear programming problems using Peña's method.
Optim. Methods Softw., 2006
2005
Comput. Optim. Appl., 2005
2004
Solving Large Scale Semidefinite Programs via an Iterative Solver on the Augmented Systems.
SIAM J. Optim., 2004
Polynomiality of an inexact infeasible interior point algorithm for semidefinite programming.
Math. Program., 2004
Convergence Analysis of an Infeasible Interior Point Algorithm Based on a Regularized Central Path for Linear Complementarity Problems.
Comput. Optim. Appl., 2004
2003
2002
A Multiple-Cut Analytic Center Cutting Plane Method for Semidefinite Feasibility Problems.
SIAM J. Optim., 2002
SIAM J. Optim., 2002
Math. Oper. Res., 2002
A Note on the Calculation of Step-Lengths in Interior-Point Methods for Semidefinite Programming.
Comput. Optim. Appl., 2002
2001
SIAM J. Matrix Anal. Appl., 2001
2000
Some New Search Directions for Primal-Dual Interior Point Methods in Semidefinite Programming.
SIAM J. Optim., 2000
1999
SIAM J. Matrix Anal. Appl., 1999
Primal-Dual Path-Following Algorithms for Determinant Maximization Problems With Linear Matrix Inequalities.
Comput. Optim. Appl., 1999
1998
1997
1996