2 citations · 5 across the 5 of their papers we have counts for
6 papers · 1 filter
A Retraction-Free EXTRA Method for Decentralized Optimization on the Stiefel Manifold
Shu Li, Jiang Hu
Decentralized optimization provides a fundamental framework for large-scale learning and signal processing with distributed data. We study decentralized optimization with orthogona…
An efficient primal dual semismooth Newton method for semidefinite programming
Zhanwang Deng, Jiang Hu, Kangkang Deng +1
In this paper, we present an efficient semismooth Newton method, named SSNCP, for solving a class of semidefinite programming problems. Our approach is rooted in an equivalent semi…
Decentralized Douglas-Rachford splitting methods for smooth optimization over compact submanifolds
Kangkang Deng, Jiang Hu, Hongxia Wang
We study decentralized smooth optimization problems over compact submanifolds. Recasting it as a composite optimization problem, we propose a decentralized Douglas-Rachford splitti…
A Brief Introduction to Manifold Optimization
Jiang Hu, Xin Liu, Zaiwen Wen +1
Manifold optimization is ubiquitous in computational and applied mathematics, statistics, engineering, machine learning, physics, chemistry and etc. One of the main challenges usua…
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
Jiang Hu, Bo Jiang, Lin Lin +2
In this paper, we study structured quasi-Newton methods for optimization problems with orthogonality constraints. Note that the Riemannian Hessian of the objective function require…
Adaptive Regularized Newton Method for Riemannian Optimization
Jiang Hu, Andre Milzarek, Zaiwen Wen +1
Optimization on Riemannian manifolds widely arises in eigenvalue computation, density functional theory, Bose-Einstein condensates, low rank nearest correlation, image registration…