Random Gradient-Free Minimization of Convex Functions

In this paper, we prove new complexity bounds for methods of convex optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors. It appears that such methods usually need at most n times more iterations than the...

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Veröffentlicht in:	Foundations of computational mathematics Jg. 17; H. 2; S. 527 - 566
Hauptverfasser:	Nesterov, Yurii, Spokoiny, Vladimir
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2017 Springer Nature B.V
Schlagworte:	Applications of Mathematics Computation Computational geometry Computer Science Convergence Convex analysis Convexity Economic models Economics Iterative methods Linear and Multilinear Algebras Math Applications in Computer Science Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Matrix Theory Nonlinear programming Numerical Analysis Optimization Texts Vectors (mathematics) 0C47 68Q25 Convex optimization Random methods 90C25 Complexity bounds Stochastic optimization Derivative-free methods
ISSN:	1615-3375, 1615-3383
Online-Zugang:	Volltext
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