Distributed optimization for degenerate loss functions arising from over-parameterization
From MaRDI portal
Publication:2060697
DOI10.1016/j.artint.2021.103575zbMath1478.68391OpenAlexW3195936333MaRDI QIDQ2060697
Publication date: 13 December 2021
Published in: Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.artint.2021.103575
Artificial neural networks and deep learning (68T07) Convex programming (90C25) Nonlinear programming (90C30) Distributed algorithms (68W15) Agent technology and artificial intelligence (68T42)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Error bounds and convergence analysis of feasible descent methods: A general approach
- Block-iterative projection methods for parallel computation of solutions to convex feasibility problems
- Distributed optimization with arbitrary local solvers
- Convergence rate analysis and error bounds for projection algorithms in convex feasibility problems
- On Projection Algorithms for Solving Convex Feasibility Problems
- Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates
- The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising
- Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
This page was built for publication: Distributed optimization for degenerate loss functions arising from over-parameterization