Publications (64)
A FISTA-type accelerated gradient algorithm for solving smooth nonconvex composite optimization problems
Jiaming Liang, Renato D. C. Monteiro, Chee-Khian Sim
In this paper, we describe and establish iteration-complexity of two accelerated composite gradient (ACG) variants to solve a smooth nonconvex composite optimization problem whose…
Improved Analysis of Restarted Accelerated Gradient and Augmented Lagrangian Methods via Inexact Proximal Point Frameworks
Matthew X. Burns, Jiaming Liang
This paper studies a class of double-loop (inner-outer) algorithms for convex composite optimization. For unconstrained problems, we develop a restarted accelerated composite gradi…
Oracle-based Uniform Sampling from Convex Bodies
Thanh Dang, Jiaming Liang
We propose new Markov chain Monte Carlo algorithms to sample a uniform distribution on a convex body . Our algorithms are based on the proximal sampler, which uses Gibbs samplin…
Universal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization
Vincent Guigues, Jiaming Liang, Renato D. C. Monteiro
This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a prox…
Dimension-Free Complexity Guarantees for Dual Dynamic Programming
Pablo Barros, Vincent Guigues, Jiaming Liang +1
This paper studies the complexity of a dual dynamic programming (DDP) method for solving a class of convex optimization problems with linear coupling constraints. Existing complexi…
Primal-dual proximal bundle and conditional gradient methods for convex problems
Jiaming Liang
This paper studies the primal-dual convergence and iteration-complexity of proximal bundle methods for solving nonsmooth problems with convex structures. More specifically, we deve…