32 citations · 36 across the 7 of their papers we have counts for
8 papers
Jointly Sparse Blind Deconvolution via Riemannian Optimization
Wenlong Wang, Baiyang Guo, Zai Yang +2
Blind deconvolution has been widely applied in system identification and signal processing. While joint sparsity commonly arises in practical scenarios, effectively exploiting this…
A Regularized Newton-Type Method for Manifold--Affine Intersection Problems under Intrinsic Transversality
Dengyu Zheng, Shixiang Chen
We propose Regularized Newton-SLRA (RN-SLRA), a regularized Newton-type method for local manifold--affine intersection problems motivated by structured low-rank approximation. Clas…
Descent-Net: Learning Descent Directions for Constrained Optimization
Zisheng Zhou, Dengyu Zheng, Zirui Chen +1
Deep learning approaches, known for their ability to model complex relationships and fast execution, are increasingly being applied to solve large optimization problems. However, e…
ADARL: Adaptive Low-Rank Structures for Robust Policy Learning under Uncertainty
Chenliang Li, Junyu Leng, Jiaxiang Li +4
Robust reinforcement learning (Robust RL) seeks to handle epistemic uncertainty in environment dynamics, but existing approaches often rely on nested min--max optimization, which i…
Local Linear Convergence of Infeasible Optimization with Orthogonal Constraints
Youbang Sun, Shixiang Chen, Alfredo Garcia +1
Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradi…
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
Shixiang Chen, Alfredo Garcia, Mingyi Hong +1
We consider a distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a…
On the Local Linear Rate of Consensus on the Stiefel Manifold
Shixiang Chen, Alfredo Garcia, Mingyi Hong +1
We study the convergence properties of Riemannian gradient method for solving the consensus problem (for an undirected connected graph) over the Stiefel manifold. The Stiefel manif…
On Distributed Non-convex Optimization: Projected Subgradient Method For Weakly Convex Problems in Networks
Shixiang Chen, Alfredo Garcia, Shahin Shahrampour
The stochastic subgradient method is a widely-used algorithm for solving large-scale optimization problems arising in machine learning. Often these problems are neither smooth nor…