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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Summary: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 determined by a discrete worst-case distribution. In this paper we obtain a less pessimistic Gauss-type bound by imposing the additional requirement that the random vector’s distribution must be unimodal. We prove that this generalized Gauss bound still admits an exact and tractable semidefinite representation. Moreover, we demonstrate that both the Chebyshev and Gauss bounds can be obtained within a unified framework using a generalized notion of unimodality. We also offer new perspectives on the computational solution of generalized moment problems, since we use concepts from Choquet theory instead of traditional duality arguments to derive semidefinite representations for worst-case probability bounds.
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ISSN:0025-5610
1436-4646
DOI:10.1007/s10107-015-0878-1