Augmented Lagrangian method for probabilistic optimization
From MaRDI portal
Publication:1931647
DOI10.1007/s10479-011-0884-5zbMath1255.90087OpenAlexW1997808314MaRDI QIDQ1931647
Gabriela Martinez, Dentcheva, Darinka
Publication date: 15 January 2013
Published in: Annals of Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10479-011-0884-5
stochastic programmingsecond-order optimality conditionschance constraintsprobabilistic constraints\(p\)-efficient points
Optimality conditions and duality in mathematical programming (90C46) Stochastic programming (90C15)
Related Items (15)
Solving joint chance constrained problems using regularization and Benders' decomposition ⋮ New reformulations for probabilistically constrained quadratic programs ⋮ Nonlinear chance constrained problems: optimality conditions, regularization and solvers ⋮ Probabilistic optimization via approximate \(p\)-efficient points and bundle methods ⋮ An Augmented Lagrangian Decomposition Method for Chance-Constrained Optimization Problems ⋮ Derivatives of probability functions: unions of polyhedra and elliptical distributions ⋮ An Inner-Outer Approximation Approach to Chance Constrained Optimization ⋮ Joint chance-constrained multi-objective multi-commodity minimum cost network flow problem with copula theory ⋮ Fuzzy Chance-Constrained Project Portfolio Selection Model Based on Credibility Theory ⋮ On the Convexity of Level-sets of Probability Functions ⋮ Exact penalization in stochastic programming -- calmness and constraint qualification ⋮ Flow-based formulations for operational fixed interval scheduling problems with random delays ⋮ Generalized Differentiation of Probability Functions Acting on an Infinite System of Constraints ⋮ Generalized differentiation of probability functions: parameter dependent sets given by intersections of convex sets and complements of convex sets ⋮ Mean-value at risk portfolio efficiency: approaches based on data envelopment analysis models with negative data and their empirical behaviour
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Dual methods for probabilistic optimization problems.
- An integer programming approach for linear programs with probabilistic constraints
- Mathematical programming approaches for generating \(p\)-efficient points
- On convexity of measures
- Probabilistic programming with discrete distributions and precedence constrained knapsack polyhedra
- \(\alpha\)-concave functions and measures and their applications
- Bounds for probabilistic integer programming problems
- Regularization methods for optimization problems with probabilistic constraints
- Lectures on Stochastic Programming
- Concavity and efficient points of discrete distributions in probabilistic programming.
This page was built for publication: Augmented Lagrangian method for probabilistic optimization
