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20192026
most citedRiemannian Smoothing Gradient Type Algorithms]{Riemannian Smoothing Gradient Type Algorithms for Nonsmooth Optimization Problem on Compact Riemannian Submanifold Embedded in Euclidean Space

7 citations · 22 across the 16 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

math.OC2023

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…

math.OC2023★ 1 cited

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…

math.OC2023★ 6 cited

New vector transport operators extending a Riemannian CG algorithm to generalized Stiefel manifold with low-rank applications

Xuejie Wang, Kangkang Deng, Zheng Peng +1

This paper proposes two innovative vector transport operators, leveraging the Cayley transform, for the generalized Stiefel manifold embedded with a non-standard metric. Specifical…

math.OC2023

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…

math.OC2023★ 4 cited

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…

math.OC2023★ 1 cited

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…

math.OC2023

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…