Generalized Gauss inequalities via semidefinite programming

A sharp upper bound on the probability of a random vector falling outside a polytope, based solely on the first and second moments of its distribution, can be computed efficiently using semidefinite programming. However, this Chebyshev-type bound tends to be overly conservative since it is determine...

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Bibliographic Details
Published in:	Mathematical programming Vol. 156; no. 1-2; pp. 271 - 302
Main Authors:	Van Parys, Bart P. G., Goulart, Paul J., Kuhn, Daniel
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016 Springer Nature B.V
Subjects:	Calculus of Variations and Optimal Control; Optimization Chebyshev approximation Combinatorics Computation Computer programming Decision theory Falling Full Length Paper Inequalities Inequality Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Optimization Probability Random variables Representations Studies Theoretical Vectors (mathematics) United States > US 90C15 Stochastic programming 90C22 Semidefinite programming
ISSN:	0025-5610, 1436-4646
Online Access:	Get full text
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