Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling-Reference-Cited by-同舟云学术

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Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling

Published:2022-04-28 Issue:2 Volume:32 Page:687-714
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Cai HanQin^ORCID,McKenzie Daniel,Yin Wotao,Zhang Zhenliang

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software,Applied Mathematics

Reference39 articles.

1. A Simple Proof of the Restricted Isometry Property for Random Matrices

2. Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

3. Stochastic Three Points Method for Unconstrained Smooth Minimization

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1. An Adaptive Variance Reduction Zeroth-Order Algorithm for Finite-Sum Optimization;Frontiers in Computing and Intelligent Systems;2023-05-17

2. Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization;Mathematics of Operations Research;2023-05

3. Stochastic zeroth-order gradient and Hessian estimators: variance reduction and refined bias bounds;Information and Inference: A Journal of the IMA;2023-04-27

4. A Hamilton–Jacobi-based proximal operator;Proceedings of the National Academy of Sciences;2023-03-29

5. Global Solutions to Nonconvex Problems by Evolution of Hamilton-Jacobi PDEs;Communications on Applied Mathematics and Computation;2023-03-20

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