8 papers · 1 filter
A Logarithmic-dual-update Bregman ADMM for Optimal Transport
Di Hou, Kim-Chuan Toh
This paper studies an entropy Bregman alternating direction method of multipliers (BADMM) with logarithmic dual update for solving discrete optimal transport (OT) problems. The alg…
An Inexact Halpern-accelerated Preconditioned Generalized Proximal Point Algorithm for the Maximal Monotone Inclusion Problem
Lei Yang, Haihang Lan, Di Hou +2
This paper studies an inexact Halpern-accelerated generalized proximal point algorithm with an admissible positive semidefinite preconditioner for solving maximal mon…
McADMM: A Multi-Clique Augmented Lagrangian-Based Algorithm for Large-Scale Sparse SDPs with Bound Constraints
Kristo Nugraha Lian, Nehal Ahmed Shaikh, Di Hou +2
sGS-PADMM [21, 14, 6] is a powerful and versatile class of convergent multi-block ADMM solvers for implementations on moderate-sized linear semidefinite programming (SDP) problems.…
Sparsity-Cone SDP Relaxations and Applications to Variable Fixing for Sparse Quadratic Programs
Di Hou, Thai P. D. Nguyen, Kim-Chuan Toh +1
Quadratic programs (QPs) with sparsity constraint are generally NP-hard, and their efficient global solution depends crucially on tractable tight convex relaxations. In this paper,…
On the efficient computation of proximal operators of affine-constrained nonconvex functions
Di Hou, Tianyun Tang, Kim-Chuan Toh +1
Proximal operators with affine constraints arise in numerous models in nonconvex projection, composite optimization, and structured regularization. However, their efficient computa…
A Low-rank Augmented Lagrangian Method for Polyhedral-SDP and Moment-SOS Relaxations of Polynomial Optimization
Di Hou, Tianyun Tang, Kim-Chuan Toh
Polynomial optimization problems (POPs) can be reformulated as geometric convex conic programs, as shown by Kim, Kojima, and Toh (SIOPT 30:1251-1273, 2020), though such formulation…
A low-rank augmented Lagrangian method for doubly nonnegative relaxations of mixed-binary quadratic programs
Di Hou, Tianyun Tang, Kim-Chuan Toh
Doubly nonnegative (DNN) programming problems are known to be challenging to solve because of their huge number of constraints and variables. In this work, we int…
A Sparse Smoothing Newton Method for Solving Discrete Optimal Transport Problems
Di Hou, Ling Liang, Kim-Chuan Toh
The discrete optimal transport (OT) problem, which offers an effective computational tool for comparing two discrete probability distributions, has recently attracted much attentio…