An extension of the quantile optimization problem with a loss function linear in random parameters

DOI10.1134/S0005117920120048zbMath1460.90120OpenAlexW3128491730MaRDI QIDQ2229545

Yu. S. Kan

Publication date: 18 February 2021

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117920120048

zbMATH Keywords

stochastic programmingextension principlequantile functionprobabilistic constraintprobability distribution kernel

Mathematics Subject Classification ID

Stochastic programming (90C15)

Related Items (1)

Parametric algorithm for finding a guaranteed solution to a quantile optimization problem

Cites Work

• A method for solving quantile optimization problems with a bilinear loss function
• Linearization method for solving quantile optimization problems with loss function depending on a vector of small random parameters
• Approximation of probabilistic constraints in stochastic programming problems with a probability measure kernel
• Algorithm to optimize the quantile criterion for the polyhedral loss function and discrete distribution of random parameters
• Sufficient conditions for optimity in problems with constraints on phase coordinates
• Methods for solving variational problems on the basis of the sufficient conditions for an absolute minimum. I
• Abnormality in the theory of necessary optimality conditions
• A Stochastic Programming Model
• Quantiles and Medians
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