7 citations · 18 across the 14 of their papers we have counts for
7 papers · 2 filters
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…