Generalized differentiation of probability functions: parameter dependent sets given by intersections of convex sets and complements of convex sets
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Publication:2115132
DOI10.1007/s00245-022-09844-5zbMath1493.90117OpenAlexW4220945801MaRDI QIDQ2115132
Wim van Ackooij, Pedro Pérez-Aros
Publication date: 15 March 2022
Published in: Applied Mathematics and Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00245-022-09844-5
Related Items
Gradient formulae for probability functions depending on a heterogenous family of constraints ⋮ Derivatives of probability functions: unions of polyhedra and elliptical distributions ⋮ Probability functions generated by set-valued mappings: a study of first order information
Cites Work
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- Decomposition algorithms for two-stage chance-constrained programs
- A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support
- A sampling-and-discarding approach to chance-constrained optimization: feasibility and Optimality
- Dual methods for probabilistic optimization problems.
- A discussion of probability functions and constraints from a variational perspective
- An integer programming approach for linear programs with probabilistic constraints
- Implementable algorithm for stochastic optimization using sample average approximations
- Mathematical programming approaches for generating \(p\)-efficient points
- Sample average approximation method for chance constrained programming: Theory and applications
- Asymptotic formulas for the derivatives of probability functions and their Monte Carlo estimations
- Robust solutions of linear programming problems contaminated with uncertain data
- Probabilistic optimization via approximate \(p\)-efficient points and bundle methods
- Properties of chance constraints in infinite dimensions with an application to PDE constrained optimization
- (Sub-)differentiability of probability functions with elliptical distributions
- Subdifferential characterization of probability functions under Gaussian distribution
- Derivatives of probability functions and integrals over sets given by inequalities
- Derivatives of probability functions and some applications
- Differentiation of probability functions: The transformation method
- Pattern definition of the \(p\)-efficiency concept
- Augmented Lagrangian method for probabilistic optimization
- Regularization methods for optimization problems with probabilistic constraints
- Lipschitz and differentiability properties of quasi-concave and singular normal distribution functions
- Gradient formulae for nonlinear probabilistic constraints with non-convex quadratic forms
- Qualification conditions-free characterizations of the \(\varepsilon \)-subdifferential of convex integral functions
- Ergodic approach to robust optimization and infinite programming problems
- Gradient formulae for probability functions depending on a heterogenous family of constraints
- New extremal principles with applications to stochastic and semi-infinite programming
- Second-order differentiability of probability functions
- Inexact stabilized Benders' decomposition approaches with application to chance-constrained problems with finite support
- On mixing sets arising in chance-constrained programming
- Extensions of stochastic optimization results to problems with system failure probability functions
- Limiting subdifferentials of indefinite integrals
- Solving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities
- Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach
- An Integer Programming and Decomposition Approach to General Chance-Constrained Mathematical Programs
- A Sample Approximation Approach for Optimization with Probabilistic Constraints
- Lectures on Stochastic Programming
- Differentiability of probability function
- Variational Analysis
- Variational Analysis and Applications
- Pattern-Based Modeling and Solution of Probabilistically Constrained Optimization Problems
- Generalized gradients for probabilistic/robust (probust) constraints
- Subdifferential Calculus Rules for Possibly Nonconvex Integral Functions
- On probabilistic capacity maximization in a stationary gas network
- Generalized Differentiation of Probability Functions Acting on an Infinite System of Constraints
- Gradient Formulae for Nonlinear Probabilistic Constraints with Gaussian and Gaussian-Like Distributions
- (Sub-)Gradient Formulae for Probability Functions of Random Inequality Systems under Gaussian Distribution
- The Scenario Approach to Robust Control Design
- Probability Gradient Estimation by Set-Valued Calculus and Applications in Network Design
- Convex Approximations of Chance Constrained Programs
- Concavity and efficient points of discrete distributions in probabilistic programming.
- Stochastic optimization of insurance portfolios for managing exposure to catastrophic risks
