Optimal Budget Allocation for Sample Average Approximation

Røyset, Johannes Ø.; Szechtman, Roberto

doi:10.1287/opre.2013.1163

Cited by 34 publications

(13 citation statements)

References 32 publications

(35 reference statements)

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“…OCBA and other Bayesian R&S procedures focus on maximizing PCS with a fixed budget and thus can be integrated with optimization procedures to reduce the effect of simulation noise on search progress. There have been a number of studies reporting integration of procedures such as OCBA with popular metaheuristics such as PSO (Horng et al, 2012) and genetic algorithms (GAs) for simulation optimization, and for SAA (Royset and Szechtman, 2013). Further efficiency can be improved by adapting OCBA to the specific comparison and selection rules of an optimization procedure.…”

Section: Ranking and Selectionmentioning

confidence: 99%

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Huang

Chen

et al. 2015

Asia Pac. J. Oper. Res.

163

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Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss * Corresponding author. how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

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Section: Ranking and Selectionmentioning

confidence: 99%

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Huang

Chen

et al. 2015

Asia Pac. J. Oper. Res.

163

View full text Add to dashboard Cite

show abstract

“…The requirements on optimality functions exactly ensure this property. Moreover, there is ample empirical indications and some theoretical evidence (see for example [3,[8][9][10]) that computational benefits accrue from approximately solving a sequence of approximating problems with increasing fidelity, each warmstarted with the previously obtained point. Optimality functions are tools to carry out such a scheme and give rise to adaptive rules for determining the timing of switches to higher-fidelity approximations.…”

Section: Introductionmentioning

confidence: 99%

Optimality Functions and Lopsided Convergence

Røyset

Wets

2015

J Optim Theory Appl

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Optimality functions pioneered by E. Polak characterize stationary points, quantify the degree with which a point fails to be stationary, and play central roles in algorithm development. For optimization problems requiring approximations, optimality functions can be used to ensure consistency in approximations, with the consequence that optimal and stationary points of the approximate problems indeed are approximately optimal and stationary for an original problem. In this paper, we review the framework and illustrate its application to nonlinear programming and other areas. Moreover, we introduce lopsided convergence of bifunctions on metric spaces and show that this notion of convergence is instrumental in establishing consistency of approximations. Lopsided convergence also leads to further characterizations of stationary points under perturbations and approximations.Keywords epi-Convergence · Lopsided convergence · Consistent approximations · Optimality functions · Optimality conditionsWe dedicate this paper to our long-time friend, colleague, collaborator, and advisor Elijah (Lucien) Polak in honor of his outstanding career and to celebrate his 85th birthday. We wish him fair weather and following snow conditions. Communicated by

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“…In those areas, given a computing budget, the goal is to optimally allocate it across different task within the simulation, and to determine the resulting rate of convergence of an estimator as the computing budget tends to infinity. The allocation may be between exploration of new points and estimation of objective function values at known points, as in global optimization [18,19] and stochastic programming [20,21], between estimation of different random variables nested by conditioning [22], or between performance estimation of different systems, as in ranking and selection [23]. Even though these studies deal with rather different applications than semi-infinite minimax problems, they motivate the present paper.…”

Section: Introductionmentioning

confidence: 99%

Rate of Convergence Analysis of Discretization and Smoothing Algorithms for Semiinfinite Minimax Problems

Røyset

Pee

2012

J Optim Theory Appl

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Discretization algorithms for semi-infinite minimax problems replace the original problem, containing an infinite number of functions, by an approximation involving a finite number, and then solve the resulting approximate problem. The approximation gives rise to a discretization error, and suboptimal solution of the approximate problem gives rise to an optimization error. Accounting for both discretization and optimization errors, we determine the rate of convergence of discretization algorithms, as a computing budget tends to infinity. We find that the rate of convergence depends on the class of optimization algorithms used to solve the approximate problem as well as the policy for selecting discretization level and number of optimization iterations. We construct optimal policies that achieve the best possible rate of convergence and find that, under certain circumstances, the better rate is obtained by inexpensive gradient methods.

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Optimal Budget Allocation for Sample Average Approximation

Cited by 34 publications

References 32 publications

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Optimality Functions and Lopsided Convergence

Rate of Convergence Analysis of Discretization and Smoothing Algorithms for Semiinfinite Minimax Problems

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