7 papers
The Augmented Lagrangian Methods: Overview and Recent Advances
Kangkang Deng, Rui Wang, Zhenyuan Zhu +2
Large-scale constrained optimization is pivotal in modern scientific, engineering, and industrial computation, often involving complex systems with numerous variables and constrain…
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 projected Riemannian stochastic recursive momentum method for nonconvex optimization
Kangkang Deng, Jiang Hu
This paper studies decentralized optimization over a compact submanifold within a communication network of nodes, where each node possesses a smooth non-convex local cost funct…
Improving the communication in decentralized manifold optimization through single-step consensus and compression
Jiang Hu, Kangkang Deng
We are concerned with decentralized optimization over a compact submanifold, where the loss functions of local datasets are defined by their respective local datasets. A key challe…
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…
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…