Optimal Budget Allocation for Sample Average Approximation
Published Online:24 May 2013https://doi.org/10.1287/opre.2013.1163
References
- . Minimizing CVaR and VaR for a portfolio of derivatives. J. Banking Finance (2006) 30:583–605Crossref, Google Scholar
- . An overview of simulation optimization via random search. Simulation (2006) (Elsevier, Amsterdam) 617–631Elsevier Handbooks in Operations Research and Management ScienceCrossref, Google Scholar
- . Metamodel-based simulation optimization. Simulation (2006) (Elsevier, Amsterdam) 535–574Elsevier Handbooks in Operations Research and Management ScienceCrossref, Google Scholar
- . An adaptive Monte Carlo algorithm for computing mixed logit estimators. Comput. Management Sci. (2006) 3(1):55–79Crossref, Google Scholar
- . Assessing solution quality in stochastic programs via sampling. Tutorials in Operations Research (2009) (INFORMS, Hanover, MD) 102–122Link, Google Scholar
- . A sequential sampling procedure for stochastic programming. Oper. Res. (2011) 59:898–913Link, Google Scholar
- . Convergence of Probability Measures (1968) (Wiley, New York) Google Scholar
- . Simulation and optimization. Tutorials in Operations Research (2008a) (INFORMS, Hanover, MD) 247–260Link, Google Scholar
- . Efficient simulation budget allocation for selecting an optimal subset. INFORMS J. Comput. (2008b) 20(4):579–595Link, Google Scholar
- . Limit theorems for simulation-based optimization via random search. ACM Trans. Modeling Comput. Simulation (2013) . ForthcomingCrossref, Google Scholar
- . Asympotic behavior of statistical estimators and of optimal solutions of stochastic optimization problems. Ann. Statist. (1988) 16(4):1517–1549Crossref, Google Scholar
- , Ermoliev Y, Wets RJ-B. Stochastic quasigradient methods. Numerical Techniques for Stochastic Optimization (1988) (Springer, New York) 141–186Crossref, Google Scholar
- . CVX: Matlab software for disciplined convex programming, version 1.21. (2010) . http://cvxr.com/cvx. Technical report, Department of Electrical Engineering, Stanford University, Stanford, CAGoogle Scholar
- . Simulation optimization using the cross-entropy method with optimal computing budget allocation. ACM Trans. Modeling Comput. Simulation (2010) 20(1):133–161Crossref, Google Scholar
- . Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (1996) (Kluwer Academic Publishers, Dordrecht, The Netherlands) Crossref, Google Scholar
- . Planning Under Uncertainty: Solving Large-scale Stochastic Linear Programs (1994) (Boyd & Fraser Publishing Company, Boston) Google Scholar
- . Stochastic Linear Programming, Models, Theory, and Computation (2005) (Springer, New York) Google Scholar
- . Selecting the best system. Simulation (2006) (Elsevier, Amsterdam) 501–534Elsevier Handbooks in Operations Research and Management ScienceCrossref, Google Scholar
- . Stochastic Approximation and Recursive Algorithms and Applications (2003) 2nd ed.(Springer, New York) Google Scholar
- . Convex optimization under inexact first-order information. (2009) . Ph.D. thesis, Georgia Institute of Technology, AtlantaGoogle Scholar
- . Computing the distribution function of a conditional expectation via Monte Carlo: Discrete conditioning spaces. ACM Trans. Modeling Comput. Simulation (2003) 13(3):238–258Crossref, Google Scholar
- . The empirical behavior of sampling methods for stochastic programming. Ann. Oper. Res. (2006) 142:215–241Crossref, Google Scholar
- . Monte Carlo bounding techniques for determining solution quality in stochastic programs. Oper. Res. Lett. (1999) 24:47–56Crossref, Google Scholar
- . Robust stochastic approximation approach to stochastic programming. SIAM J. Optim. (2009) 19(4):1574–1609Crossref, Google Scholar
- . Introductory Lectures on Convex Optimization (2004) (Kluwer Academic Publishers, Boston) Crossref, Google Scholar
- . Metaheuristics. Simulation (2006) (Elsevier, Amsterdam) 633–654Elsevier Handbooks in Operations Research and Management ScienceCrossref, Google Scholar
- . On choosing parameters in retrospective-approximation algorithms for stochastic root finding and simulation optimization. Oper. Res. (2010) 58(4):889–901Link, Google Scholar
- . Efficient sample sizes in stochastic nonlinear programming. J. Comput. Appl. Math. (2008) 217:301–310Crossref, Google Scholar
- . On buffered failure probability in design and optimization of structures. Reliability Engrg. System Safety (2010) 95:499–510Crossref, Google Scholar
- . Conditional value-at-risk for general loss distributions. J. Banking Finance (2002) 26:1443–1471Crossref, Google Scholar
- . Optimality functions in stochastic programming. Math. Programming (2012) 135(1):293–321Crossref, Google Scholar
- . Lectures on Stochastic Programming: Modeling and Theory (2009) (Society for Industrial and Applied Mathematics, Philadelphia) Crossref, Google Scholar
- . The sample average approximation method applied to stochastic routing problems: A computational study. Comput. Optim. Appl. (2003) 24:289–333Crossref, Google Scholar
- . Smooth sample average approximation of stationary points in nonsmooth stochastic optimization and applications. Math. Programming (2009) 119:371–401Crossref, Google Scholar

