5 papers · 1 filter
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…
Distributed Stochastic Proximal Algorithm on Riemannian Submanifolds for Weakly-convex Functions
Jishu Zhao, Xi Wang, Jinlong Lei +1
This paper aims to investigate the distributed stochastic optimization problems on compact embedded submanifolds (in the Euclidean space) where the local cost functions are weakly-…
A Geometry-Adaptive Regularized Newton-Type Method for Manifold-Affine Intersection Problems
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…
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…