Solving Chance-Constrained Problems via a Smooth Sample-Based Nonlinear Approximation

DOI10.1137/19M1261985zbMath1448.90065arXiv1905.07377OpenAlexW3048727147MaRDI QIDQ5116556

Alejandra Peña-Ordieres, Andreas Wächter, James R. Luedtke

Publication date: 18 August 2020

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.07377

zbMATH Keywords

smoothingsequential quadratic programmingnonlinear optimizationchance constraintstrust regionquantile functionsample average approximation

Mathematics Subject Classification ID

Nonlinear programming (90C30) Stochastic programming (90C15) Methods of successive quadratic programming type (90C55)

Related Items

ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs, Nonconvex and nonsmooth approaches for affine chance-constrained stochastic programs, Optimization under Rare Chance Constraints, Gradients and subgradients of buffered failure probability, Derivatives of probability functions: unions of polyhedra and elliptical distributions, Data-Driven Approximation of Contextual Chance-Constrained Stochastic Programs, On data-driven chance constraint learning for mixed-integer optimization problems, Chance-constrained optimization under limited distributional information: a review of reformulations based on sampling and distributional robustness, Approximate methods for solving chance-constrained linear programs in probability measure space, Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency, Statistical learning for probability-constrained stochastic optimal control, A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs

Uses Software

• OR-Library

Cites Work

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