Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently
From MaRDI portal
Publication:5001827
DOI10.1109/TIT.2021.3075148zbMath1475.94044arXiv1911.11167OpenAlexW3157991651MaRDI QIDQ5001827
Publication date: 23 July 2021
Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.11167
Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Methods of reduced gradient type (90C52)
Related Items (2)
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs ⋮ Recent Theoretical Advances in Non-Convex Optimization
This page was built for publication: Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently