Stopping rules for class of sampling-based stochastic programming algorithms - Info and Reading Options
By David P. Morton

"Stopping rules for class of sampling-based stochastic programming algorithms" was published by Naval Postgraduate School in 1994 - Monterey, Calif, it has 24 pages and the language of the book is English.
“Stopping rules for class of sampling-based stochastic programming algorithms” Metadata:
- Title: ➤ Stopping rules for class of sampling-based stochastic programming algorithms
- Author: David P. Morton
- Language: English
- Number of Pages: 24
- Publisher: Naval Postgraduate School
- Publish Date: 1994
- Publish Location: Monterey, Calif
“Stopping rules for class of sampling-based stochastic programming algorithms” Subjects and Themes:
- Subjects: LINEAR PROGRAMMING - STOPPING RULES(MATHEMATICS)
Edition Specifications:
- Pagination: 24 p. :
Edition Identifiers:
- The Open Library ID: OL25497269M - OL16874634W
AI-generated Review of “Stopping rules for class of sampling-based stochastic programming algorithms”:
"Stopping rules for class of sampling-based stochastic programming algorithms" Description:
The Open Library:
Decomposition and Monte Carlo sampling-based algorithms hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. In this paper, we develop a stopping rule theory for a class of algorithms that estimate bounds on the optimal objective function value by sampling. We provide rules for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence intervals for the quality of the proposed solution can be verified. These rules are applied to a multistage stochastic linear programming algorithm due to Pereira and Pinto. Stopping rules, Monte Carlo sampling, Stochastic programming.
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