7 citations · 22 across the 16 of their papers we have counts for
17 papers
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…
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…
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…
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 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…
A Decomposition Augmented Lagrangian Method for Low-rank Semidefinite Programming
Yifei Wang, Kangkang Deng, Haoyang Liu +1
We develop a decomposition method based on the augmented Lagrangian framework to solve a broad family of semidefinite programming problems, possibly with nonlinear objective functi…
An Inexact Manifold Augmented Lagrangian Method for Adaptive Sparse Canonical Correlation Analysis with Trace Lasso Regularization
Kangkang Deng, Zheng Peng
Canonical correlation analysis (CCA for short) describes the relationship between two sets of variables by finding some linear combinations of these variables that maximizing the c…
An inexact augmented Lagrangian method for nonsmooth optimization on Riemannian manifold
Deng Kangkang, Peng Zheng
We consider a nonsmooth optimization problem on Riemannian manifold, whose objective function is the sum of a differentiable component and a nonsmooth convex function. We propose a…