9 citations · 27 across the 15 of their papers we have counts for
16 papers
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…
Oracle complexities of augmented Lagrangian methods for nonsmooth manifold optimization
Kangkang Deng, Jiang Hu, Jiayuan Wu +1
In this paper, we present two novel manifold inexact augmented Lagrangian methods, \textbf{ManIAL} for deterministic settings and \textbf{StoManIAL} for stochastic settings, solvin…
An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-Block Composite Optimization
Zhanwang Deng, Kangkang Deng, Jiang Hu +1
In this paper, we develop a novel primal-dual semismooth Newton method for solving linearly constrained multi-block convex composite optimization problems. First, a differentiable…
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…
Achieving Consensus over Compact Submanifolds
Jiang Hu, Jiaojiao Zhang, Kangkang Deng
We consider the consensus problem in a decentralized network, focusing on a compact submanifold that acts as a nonconvex constraint set. By leveraging the proximal smoothness of th…
Decentralized projected Riemannian gradient method for smooth optimization on compact submanifolds
Kangkang Deng, Jiang Hu
We consider the problem of decentralized nonconvex optimization over a compact submanifold, where each local agent's objective function defined by the local dataset is smooth. Leve…
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
Jinxin Wang, Jiang Hu, Shixiang Chen +2
We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of agents cooperatively minimize a fin…
Decentralized Riemannian natural gradient methods with Kronecker-product approximations
Jiang Hu, Kangkang Deng, Na Li +1
With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problem…
A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity
Jiang Hu, Kangkang Deng, Jiayuan Wu +1
This paper aims to develop a Newton-type method to solve a class of nonconvex composite programs. In particular, the nonsmooth part is possibly nonconvex. To tackle the nonconvexit…
Riemannian Smoothing Gradient Type Algorithms]{Riemannian Smoothing Gradient Type Algorithms for Nonsmooth Optimization Problem on Compact Riemannian Submanifold Embedded in Euclidean Space
Zheng Peng, Weihe Wu, Jiang Hu +1
In this paper, we introduce the notion of generalized -stationarity for a class of nonconvex and nonsmooth composite minimization problems on compact Riemannian submanifold embe…
On the local convergence of the semismooth Newton method for composite optimization
Jiang Hu, Tonghua Tian, Shaohua Pan +1
In this paper, we consider a large class of nonlinear equations derived from first-order type methods for solving composite optimization problems. Traditional approaches to establi…
Riemannian Natural Gradient Methods
Jiang Hu, Ruicheng Ao, Anthony Man-Cho So +2
This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in var…
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…