Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points

In this paper, we propose and analyze zeroth-order stochastic approximation algorithms for nonconvex and convex optimization, with a focus on addressing constrained optimization, high-dimensional setting, and saddle point avoiding. To handle constrained optimization, we first propose generalizations...

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Bibliographic Details
Published in:	Foundations of computational mathematics Vol. 22; no. 1; pp. 35 - 76
Main Authors:	Balasubramanian, Krishnakumar, Ghadimi, Saeed
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2022 Springer Nature B.V
Subjects:	Algorithms Applications of Mathematics Computational geometry Computer Science Constraints Convergence Convexity Economics Hessian matrices Image processing Linear and Multilinear Algebras Math Applications in Computer Science Mathematical analysis Mathematics Mathematics and Statistics Matrix Theory Newton methods Numerical Analysis Optimization Regularization Saddle points Smoothing Sparsity Nonconvex optimization 65K05 90C56 Conditional gradient methods Complexity bounds 90C15 90C26 49M15 Stochastic optimization Zeroth-order methods Newton method
ISSN:	1615-3375, 1615-3383
Online Access:	Get full text
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