A regularized decomposition method for minimizing a sum of polyhedral functions
Mathematical Programming: Series A and B
Parallel processors for planning under uncertainty
Annals of Operations Research
Asymptotic analysis of stochastic programs
Annals of Operations Research
Statistical verification of optimality conditions for stochastic programs with recourse
Annals of Operations Research
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Annals of Operations Research - Special issue on sensitivity analysis and optimization of discrete event systems
Asymptotic theory for solutions in statistical estimation and stochastic programming
Mathematics of Operations Research
Duality and statistical tests of optimality for two stage stochastic programs
Mathematical Programming: Series A and B
A simulation-based approach to two-stage stochastic programming with recourse
Mathematical Programming: Series A and B
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Computational Optimization and Applications
Computers and Operations Research
A testbed of simulation-optimization problems
Proceedings of the 38th conference on Winter simulation
The "BEST" algorithm for solving stochastic mixed integer programs
Proceedings of the 38th conference on Winter simulation
Jackknife estimators for reducing bias in asset allocation
Proceedings of the 38th conference on Winter simulation
Sequential sampling for solving stochastic programs
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Confidence level solutions for stochastic programming
Automatica (Journal of IFAC)
The smoothed Monte Carlo method in robustness optimization
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Enhancements of two-stage stochastic decomposition
Computers and Operations Research
The mathematics of continuous-variable simulation optimization
Proceedings of the 40th Conference on Winter Simulation
Arc-Routing Models for Small-Package Local Routing
Transportation Science
Simulation-based approach to estimation of latent variable models
Computational Statistics & Data Analysis
Computers and Operations Research
Integration and coordination of multirefinery networks: a robust optimization approach
MS '08 Proceedings of the 19th IASTED International Conference on Modelling and Simulation
Stochastic Root Finding and Efficient Estimation of Convex Risk Measures
Operations Research
Expert Systems with Applications: An International Journal
Computers and Operations Research
Estimating the efficient frontier of a probabilistic bicriteria model
Winter Simulation Conference
A Sequential Sampling Procedure for Stochastic Programming
Operations Research
The impact of sampling methods on bias and variance in stochastic linear programs
Computational Optimization and Applications
Computational Optimization and Applications
On complexity of multistage stochastic programs
Operations Research Letters
Designing Optimal Spectral Filters for Inverse Problems
SIAM Journal on Scientific Computing
A unified method for handling discrete and continuous uncertainty in Bayesian Stackelberg games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the Winter Simulation Conference
Overlapping batches for the assessment of solution quality in stochastic programs
Proceedings of the Winter Simulation Conference
On sample size control in sample average approximations for solving smooth stochastic programs
Computational Optimization and Applications
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
A two-stage approach to the orienteering problem with stochastic weights
Computers and Operations Research
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A stochastic program SP with solution value z^* can be approximately solved by sampling n realizations of the program's stochastic parameters, and by solving the resulting ''approximating problem'' for (x^*"n,z^*"n). We show that, in expectation, z^*"n is a lower bound on z^* and that this bound monotonically improves as n increases. The first result is used to construct confidence intervals on the optimality gap for any candidate solution x@^ to SP, e.g., x@^=x^*"n. A sampling procedure based on common random numbers ensures nonnegative gap estimates and provides significant variance reduction over naive sampling on four test problems.