Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms-Reference-Cited by-同舟云学术

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Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms

Published:2019-01 Issue:1 Volume:67 Page:250-266
ISSN:0030-364X
Container-title:Operations Research
language:en
Short-container-title:Operations Research

Author:

L. Salemi Peter¹^ORCID,Song Eunhye²^ORCID,Nelson Barry L.³^ORCID,Staum Jeremy³^ORCID

Affiliation:

1. The MITRE Corporation, McLean, Virginia 22102;

2. Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802;

3. Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications

Reference19 articles.

1. On computing certain elements of the inverse of a sparse matrix

2. Tutorial: Optimization via simulation with Bayesian statistics and dynamic programming

3. The Knowledge-Gradient Policy for Correlated Normal Beliefs

4. Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models

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1. Marginal improvement procedures for top-m selection;Automatica;2024-12

2. Projected Gaussian Markov Improvement Algorithm for High-Dimensional Discrete Optimization via Simulation;ACM Transactions on Modeling and Computer Simulation;2024-05-16

3. The (Surprising) Sample Optimality of Greedy Procedures for Large-Scale Ranking and Selection;Management Science;2024-05-07

4. Sequential Selection for Minimizing the Variance with Application to Crystallization Experiments;The American Statistician;2024-03-07

5. Approximate Gaussian Process Regression with Pairwise Comparison Data;2023 Winter Simulation Conference (WSC);2023-12-10

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