Quadratically constrained minimum cross-entropy analysis

DOI10.1007/BF01587079zbMath0671.90071OpenAlexW2067885730MaRDI QIDQ1119476

J. R. Rajasekera, Shu-Cherng Fang

Publication date: 1989

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01587079

zbMATH Keywords

perturbationdualityapproximationgeometric inequalitiesminimum cross-entropy problemsnonsmooth concave linearly constrained dual problemquadratic (convex) constraints

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Duality theory (optimization) (49N15) Measures of information, entropy (94A17) Mathematical programming (90C99)

Related Items (11)

Linear programming with entropic perturbation ⋮ A quadratically convergent global algorithm for the linearly-constrained minimum cross-entropy problem ⋮ An unconstrained convex programming view of linear programming ⋮ An efficient computational procedure for solving entropy optimization problems with infinitely many linear constraints ⋮ Minimum cross-entropy analysis with entropy-type constraints ⋮ On the optimal entropy analysis ⋮ Linearly constrained estimation by mathematical programming ⋮ Minimum discrimination information problems via generalized geometric programming. ⋮ Proximal minimization algorithm with \(D\)-functions ⋮ Deriving an unconstrained convex program for linear programming ⋮ Interior-point algorithm for quadratically constrained entropy minimization problems

Uses Software

• MINOS

Cites Work

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• Minimum cross-entropy spectral analysis
• Controlled dual perturbations for l p -programming
• Quadratically Constrained Information Theoretic Analysis
• Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
• Extremal principles and optimization dualities for khinchin-kullback-leibler estimation
• Controlled perturbations for quadratically constrained quadratic programs
• Convex Analysis

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