A Framework for Solving Chance-Constrained Linear Matrix Inequality Programs
From MaRDI portal
Publication:5085474
DOI10.1287/ijoc.2020.0982OpenAlexW3093357292MaRDI QIDQ5085474
Miguel A. Lejeune, Roya Karimi, Jianqiang Cheng
Publication date: 27 June 2022
Published in: INFORMS Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1287/ijoc.2020.0982
stochastic programminglinear matrix inequalitiessemidefinite programmingchance-constrained programmingsampling-based approximation
Cites Work
- Unnamed Item
- Nonlinear chance constrained problems: optimality conditions, regularization and solvers
- Chance-constrained economic dispatch with renewable energy and storage
- On approximate solutions for robust convex semidefinite optimization problems
- Uncertain convex programs: randomized solutions and confidence levels
- Distributionally robust joint chance constraints with second-order moment information
- Partial sample average approximation method for chance constrained problems
- Lectures on Modern Convex Optimization
- On Tractable Approximations of Uncertain Linear Matrix Inequalities Affected by Interval Uncertainty
- Solving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities
- From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
- Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach
- On Safe Tractable Approximations of Chance-Constrained Linear Matrix Inequalities
- A Sample Approximation Approach for Optimization with Probabilistic Constraints
- Lectures on Stochastic Programming
- Robust Solutions to Uncertain Semidefinite Programs
- Robust Truss Topology Design via Semidefinite Programming
- Continuous-time analysis, eigenstructure assignment, and H/sub 2/ synthesis with enhanced linear matrix inequalities (LMI) characterizations
- A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints
- Linear Matrix Inequalities with Stochastically Dependent Perturbations and Applications to Chance-Constrained Semidefinite Optimization
- Strong duality in robust semi-definite linear programming under data uncertainty
- 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
- Convex Approximations of Chance Constrained Programs
- Relaxations for Robust Linear Matrix Inequality Problems with Verifications for Exactness
- Extended Matrix Cube Theorems with Applications to μ-Theory in Control
- Handbook of semidefinite programming. Theory, algorithms, and applications
This page was built for publication: A Framework for Solving Chance-Constrained Linear Matrix Inequality Programs
