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Robust Optimization and Distributionally Robust Optimization
This module provides robust optimization methods that hedge against uncertainty in problem data. These algorithms are essential for risk-aware decision making under uncertainty.
§Uncertainty Models
- Box uncertainty: Each parameter perturbed independently within ±δ
- Ellipsoidal uncertainty: Parameters perturbed inside an ellipsoid defined by a covariance matrix
- Polyhedral uncertainty: Parameters perturbed within a polytope (Aξ ≤ b)
- Budgeted uncertainty: Bertsimas-Sim model with total budget Γ
§Algorithms
box_robust: Worst-case evaluation over box uncertainty setellipsoidal_robust: Worst-case evaluation over ellipsoidal uncertainty setdistributionally_robust_cvar: CVaR-based distributionally robust objectivesaa_solve: Sample Average Approximation (Kleywegt-Shapiro-Homem-de-Mello)
§Sub-modules
minimax: Minimax optimization (subgradient, bundle, Nesterov smoothing, fictitious play)robust_lp: Robust linear programming (box, ellipsoidal, budgeted uncertainty)worst_case: Worst-case analysis, AARC, scenario approach, Wasserstein DRO
§References
- Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization.
- Bertsimas, D. & Sim, M. (2004). “The price of robustness”. Operations Research.
- Shapiro, A., Dentcheva, D., & Ruszczyński, A. (2014). Lectures on Stochastic Programming.
Modules§
- minimax
- Minimax Optimization Methods for Robust Optimization
- robust_
lp - Robust Linear Programming
- worst_
case - Worst-Case Analysis and Scenario-Based Robust Optimization
Structs§
- Robust
Config - High-level configuration for robust optimization.
- Robust
Problem - Configuration for a robust optimization problem.
- SaaConfig
- Options for the SAA solver.
- SaaResult
- Result of a Sample Average Approximation solve.
Enums§
- Uncertainty
Set - Describes the geometry of an uncertainty set for robust optimization.
- Uncertainty
Type - Type of uncertainty set used in robust optimization.
Functions§
- box_
robust - Evaluate the worst-case objective over an axis-aligned box uncertainty set.
- distributionally_
robust_ cvar - Distributionally robust CVaR (Conditional Value-at-Risk) objective.
- ellipsoidal_
robust - Evaluate the worst-case objective over an ellipsoidal uncertainty set.
- saa_
solve - Solve a stochastic program via Sample Average Approximation (SAA).