2 citations · 3 across the 6 of their papers we have counts for
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A Fixed-Penalty Linearized Augmented Lagrangian Method with Classical Multiplier Updates
Benqi Liu, Kangkang Deng, Zichen Wang +1
Augmented Lagrangian methods are effective for nonlinear equality-constrained optimization, but solving their nonlinear primal subproblems can be expensive. For smooth nonconvex pr…
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…
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…