A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs
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Publication:2063194
DOI10.1007/s12532-020-00199-yzbMath1477.90049arXiv1812.07066OpenAlexW3124036821MaRDI QIDQ2063194
Rohit Kannan, James R. Luedtke
Publication date: 10 January 2022
Published in: Mathematical Programming Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1812.07066
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- Data-driven chance constrained stochastic program
- Fast projection onto the simplex and the \(l_1\) ball
- Nonlinear chance constrained problems: optimality conditions, regularization and solvers
- On the quantification of nomination feasibility in stationary gas networks with random load
- A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support
- On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints
- A smoothing function approach to joint chance-constrained programs
- A sampling-and-discarding approach to chance-constrained optimization: feasibility and Optimality
- Solving joint chance constrained problems using regularization and Benders' decomposition
- Partitioning procedures for solving mixed-variables programming problems
- On nonsmooth and discontinuous problems of stochastic systems optimization
- Stochastic generalized gradient method for nonconvex nonsmooth stochastic optimization
- Probabilistic optimization via approximate \(p\)-efficient points and bundle methods
- Uncertain convex programs: randomized solutions and confidence levels
- Regularization methods for optimization problems with probabilistic constraints
- Research on probabilistic methods for control system design
- Stochastic subgradient method converges on tame functions
- Efficiency of minimizing compositions of convex functions and smooth maps
- Inexact stabilized Benders' decomposition approaches with application to chance-constrained problems with finite support
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
- Convergence analysis on a smoothing approach to joint chance constrained programs
- Chance-Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty
- Julia: A Fresh Approach to Numerical Computing
- From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
- Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach
- A Sample Approximation Approach for Optimization with Probabilistic Constraints
- Lectures on Stochastic Programming
- Robust Stochastic Approximation Approach to Stochastic Programming
- Variational Analysis
- A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints
- The Minimization of Semicontinuous Functions: Mollifier Subgradients
- Solving Chance-Constrained Problems via a Smooth Sample-Based Nonlinear Approximation
- 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
- Probabilistically Constrained Linear Programs and Risk-Adjusted Controller Design
- Chance Constrained Programming with Joint Constraints
- An Inner-Outer Approximation Approach to Chance Constrained Optimization
- Convex Approximations of Chance Constrained Programs
- Probability Content of Regions Under Spherical Normal Distributions, IV: The Distribution of Homogeneous and Non-Homogeneous Quadratic Functions of Normal Variables
- JuMP: A Modeling Language for Mathematical Optimization
- Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
