A constraint sampling approach for multi-stage robust optimization

DOI10.1016/j.automatica.2011.12.002zbMath1244.93097OpenAlexW1988431852MaRDI QIDQ445078

Daniel Kuhn, Phebe Vayanos, Berc Rustem

Publication date: 24 August 2012

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2011.12.002

zbMATH Keywords

decision rulesscenario approximationmulti-stage robust optimizationviolation probability

Mathematics Subject Classification ID

Adaptive control/observation systems (93C40) Sampled-data control/observation systems (93C57) Stochastic systems in control theory (general) (93E03)

Related Items (15)

Scenario Min-Max Optimization and the Risk of Empirical Costs ⋮ ROC++: Robust Optimization in C++ ⋮ Multipolar robust optimization ⋮ Robust combinatorial optimization under convex and discrete cost uncertainty ⋮ Wait-and-judge scenario optimization ⋮ Recent advances in robust optimization: an overview ⋮ The decision rule approach to optimization under uncertainty: methodology and applications ⋮ Oracle-based algorithms for binary two-stage robust optimization ⋮ Varying confidence levels for CVaR risk measures and minimax limits ⋮ The wait-and-judge scenario approach applied to antenna array design ⋮ The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations ⋮ Randomized methods for design of uncertain systems: sample complexity and sequential algorithms ⋮ Robust optimization for lot-sizing problems under yield uncertainty ⋮ Risk and complexity in scenario optimization ⋮ Two-stage linear decision rules for multi-stage stochastic programming

Uses Software

• ROME

Cites Work

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