Optimization of Algorithmic Parameters using a Meta-Control Approach*

Kohn, Wolf; Zabinsky, Zelda B.; Brayman, Vladimir

doi:10.1007/s10898-005-1655-0

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…A similar perspective is taken in [17] in the context of interior-point methods for solving deterministic nonlinear programs and in [21] for interacting-particle algorithms. Since SAA with sample average problems solved by nonlinear programming algorithms represent a substantial departure from those contexts, we are unable to build on those studies.…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 94%

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On Sample Size Control in Sample Average Approximations for Solving Smooth Stochastic Programs

Røyset¹

2009

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Abstract. We consider smooth stochastic programs and develop a discrete-time optimal-control problem for adaptively selecting sample sizes in a class of algorithms based on sample average approximations (SAA). The control problem aims to minimize the expected computational cost to obtain a near-optimal solution of a stochastic program and is solved approximately using dynamic programming. The optimal-control problem depends on unknown parameters such as rate of convergence, computational cost per iteration, and sampling error. Hence, we implement the approach within a receding-horizon framework where parameters are estimated and the optimalcontrol problem is solved repeatedly during the calculations of a SAA algorithm. The resulting sample-size selection policy consistently produces near-optimal solutions in short computing times as compared to other plausible policies in several numerical examples.

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Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 94%

“…We find similar efforts to efficiently control the precision of function (and gradient) evaluations or other algorithm parameters in the areas of semi-infinite programming [9], interior-point methods [17], interacting-particle algorithms [21], and simulated annealing [22].…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

On Sample Size Control in Sample Average Approximations for Solving Smooth Stochastic Programs

Røyset¹

2009

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“…A similar perspective is taken in [20] in the context of interior-point methods for solving deterministic nonlinear programs and in [25] for interacting-particle algorithms. Since the VSAA approach with sample average problems solved by nonlinear programming algorithms represent a substantial departure from those contexts, we are unable to build on those studies.…”

Section: Introductionmentioning

confidence: 94%

“…We find similar efforts to efficiently control the precision of function (and gradient) evaluations and algorithm parameters in other areas such as for instance semi-infinite programming [12], interior-point methods [20], interacting-particle algorithms [25], and simulated annealing [26].…”

Section: Introductionmentioning

confidence: 99%

On sample size control in sample average approximations for solving smooth stochastic programs

Røyset

2013

Comput Optim Appl

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Abstract. We consider smooth stochastic programs and develop a discrete-time optimal-control problem for adaptively selecting sample sizes in a class of algorithms based on variable sample average approximations (VSAA). The control problem aims to minimize the expected computational cost to obtain a near-optimal solution of a stochastic program and is solved approximately using dynamic programming. The optimal-control problem depends on unknown parameters such as rate of convergence, computational cost per iteration, and sampling error. Hence, we implement the approach within a receding-horizon framework where parameters are estimated and the optimalcontrol problem is solved repeatedly during the calculations of a VSAA algorithm. The resulting sample-size selection policy consistently produces near-optimal solutions in short computing times as compared to other plausible policies in several numerical examples.

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“…Enterprise dynamics is one practical situation that gives rise to this type of variational problem (see [1,2]). While solutions to the variational problems of interest may not exist in the classical sense, due to a lack of intrinsic properties, solutions to a relaxation of the variational problem do exist.…”

Section: Introductionmentioning

confidence: 99%