Monte Carlo simulation approach to stochastic programming
Proceedings of the 33nd conference on Winter simulation
Decomposition Algorithms for Stochastic Programming on a Computational Grid
Computational Optimization and Applications
Computational Optimization and Applications
Quantitative Stability in Stochastic Programming: The Method of Probability Metrics
Mathematics of Operations Research
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A stochastic programming approach to scheduling in TAC SCM
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Bidding under uncertainty: theory and experiments
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Provably near-optimal sampling-based algorithms for Stochastic inventory control models
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
On choosing parameters in retrospective-approximation algorithms for simulation-optimization
Proceedings of the 38th conference on Winter simulation
Stochastic Integer Programming: Limit Theorems and Confidence Intervals
Mathematics of Operations Research
Confidence level solutions for stochastic programming
Automatica (Journal of IFAC)
An Integer Programming Approach for Linear Programs with Probabilistic Constraints
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Probabilistic analysis of simulation-based games
ACM Transactions on Modeling and Computer Simulation (TOMACS)
OR PRACTICE---R&D Project Portfolio Analysis for the Semiconductor Industry
Operations Research
Mathematics of Operations Research
A Distributional Interpretation of Robust Optimization
Mathematics of Operations Research
Stochastic iterative dynamic programming: a Monte Carlo approach to dual control
Automatica (Journal of IFAC)
Risk and Energy Consumption Tradeoffs in Cloud Computing Service via Stochastic Optimization Models
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Robust Simulation of Global Warming Policies Using the DICE Model
Management Science
Convergence properties of direct search methods for stochastic optimization
Proceedings of the Winter Simulation Conference
Stochastic Nash equilibrium problems: sample average approximation and applications
Computational Optimization and Applications
| Hi-index | 0.00 |
In this paper we discuss Monte Carlo simulation based approximations of a stochastic programming problem. We show that if the corresponding random functions are convex piecewise linear and the distribution is discrete, then an optimal solution of the approximating problem provides an exact optimal solution of the true problem with probability one for sufficiently large sample size. Moreover, by using the theory of large deviations, we show that the probability of such an event approaches one exponentially fast with increase of the sample size. In particular, this happens in the case of linear two- (or multi-) stage stochastic programming with recourse if the corresponding distributions are discrete. The obtained results suggest that, in such cases, Monte Carlo simulation based methods could be very efficient. We present some numerical examples to illustrate the ideas involved.