### Abstract

Available at optimization-online.org

Original language | English (US) |
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Number of pages | 13 |

State | Published - 2017 |

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### Cite this

*Sample Average Approximation with Adaptive Importance Sampling*.

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*Sample Average Approximation with Adaptive Importance Sampling*.

**Sample Average Approximation with Adaptive Importance Sampling.** / Waechter, Andreas; Staum, Jeremy C; Maggiar, Alvaro; Feng, Mingbin.

Research output: Book/Report › Other report

TY - BOOK

T1 - Sample Average Approximation with Adaptive Importance Sampling

AU - Waechter, Andreas

AU - Staum, Jeremy C

AU - Maggiar, Alvaro

AU - Feng, Mingbin

N1 - http://www.optimization-online.org/DB_FILE/2017/04/5951.pdf

PY - 2017

Y1 - 2017

N2 - We study sample average approximations under adaptive importance sampling in which the sample densities may depend on previous random samples. Based on a generic uniform law of large numbers, we establish uniform convergence of the sample average approximation to the true function. We obtain convergence of the optimal value and optimal solutions of the sample average approximation. The relevance of this result is demonstrated in the context of the convergence analysis of a randomized optimization algorithm.Available at optimization-online.org

AB - We study sample average approximations under adaptive importance sampling in which the sample densities may depend on previous random samples. Based on a generic uniform law of large numbers, we establish uniform convergence of the sample average approximation to the true function. We obtain convergence of the optimal value and optimal solutions of the sample average approximation. The relevance of this result is demonstrated in the context of the convergence analysis of a randomized optimization algorithm.Available at optimization-online.org

M3 - Other report

BT - Sample Average Approximation with Adaptive Importance Sampling

ER -