Dual Randomized Coordinate Descent Method for Solving a Class of Nonconvex Problems

DOI10.1137/20M133926XzbMath1472.90094OpenAlexW3185466945MaRDI QIDQ5003218

Amir Beck, Marc Teboulle

Publication date: 20 July 2021

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/20m133926x

zbMATH Keywords

stationarityToland dualitydual coordinate descent

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Optimality conditions and duality in mathematical programming (90C46)

Cites Work

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• On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
• Duality in nonconvex optimization
• A duality principle for non-convex optimisation and the calculus of variations
• DC programming: overview.
• Computing B-Stationary Points of Nonsmooth DC Programs
• First-Order Methods in Optimization
• Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint
• On the Convergence to Stationary Points of Deterministic and Randomized Feasible Descent Directions Methods
• Convex Analysis