Non-convex scenario optimization

Decision theory (91B06) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05) Stochastic programming (90C15) General considerations in statistical decision theory (62C05)

Cites Work

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A sampling-and-discarding approach to chance-constrained optimization: feasibility and Optimality
Wait-and-judge scenario optimization
On the sample size of random convex programs with structured dependence on the uncertainty
Interval predictor models: identification and reliability
Expected shortfall: heuristics and certificates
Classification with guaranteed probability of error
A theory of the risk for empirical CVaR with application to portfolio selection
Risk and complexity in scenario optimization
On the quantification of aleatory and epistemic uncertainty using sliced-normal distributions
Optimal disturbance compensation for constrained linear systems operating in stationary conditions: a scenario-based approach
On a class of interval predictor models with universal reliability
A randomized relaxation method to ensure feasibility in stochastic control of linear systems subject to state and input constraints
The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations
Randomized methods for design of uncertain systems: sample complexity and sequential algorithms
Random Convex Programs with $L_1$-Regularization: Sparsity and Generalization
FAST—Fast Algorithm for the Scenario Technique
A Scenario Approach for Non-Convex Control Design
On the Connection Between Compression Learning and Scenario Based Single-Stage and Cascading Optimization Problems
Performance Bounds for the Scenario Approach and an Extension to a Class of Non-Convex Programs
On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
Scenario Min-Max Optimization and the Risk of Empirical Costs
A Sample Approximation Approach for Optimization with Probabilistic Constraints
A General Scenario Theory for Nonconvex Optimization and Decision Making
From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming
A Coverage Theory for Least Squares
Introduction to the Scenario Approach
A Posteriori Probabilistic Bounds of Convex Scenario Programs With Validation Tests
Probably Approximately Correct Nash Equilibrium Learning
Randomized Strategies for Probabilistic Solutions of Uncertain Feasibility and Optimization Problems
RANDOM PREDICTOR MODELS FOR RIGOROUS UNCERTAINTY QUANTIFICATION
Scenario Approach for Minmax Optimization with Emphasis on the Nonconvex Case: Positive Results and Caveats
The Scenario Approach to Robust Control Design
Randomized Solutions to Convex Programs with Multiple Chance Constraints
On the Exact Feasibility of Convex Scenario Programs With Discarded Constraints
Complexity Is an Effective Observable to Tune Early Stopping in Scenario Optimization
On the Sensitivity of Linear Resource Sharing Problems to the Arrival of New Agents

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