A stochastic subspace approach to gradient-free optimization in high dimensions
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Publication:2044475
DOI10.1007/s10589-021-00271-wzbMath1473.90087arXiv2003.02684OpenAlexW3152501190MaRDI QIDQ2044475
Alireza Doostan, David Kozak, Luis Tenorio, Stephen R. Becker
Publication date: 9 August 2021
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.02684
Large-scale problems in mathematical programming (90C06) Numerical optimization and variational techniques (65K10) Computational methods for problems pertaining to systems and control theory (93-08)
Related Items
Zeroth-order algorithms for stochastic distributed nonconvex optimization, Scalable subspace methods for derivative-free nonlinear least-squares optimization, Zeroth-order optimization with orthogonal random directions, Direct Search Based on Probabilistic Descent in Reduced Spaces, Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions
Uses Software
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