Researcher ORCID Identifier
https://orcid.org/0000-0003-2188-8558
Graduation Year
2016
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Mathematics
Reader 1
Dr. Mark Huber
Terms of Use & License Information
Rights Information
© John M Fowler
Abstract
This thesis provides an overview of stochastic optimization (SP) problems and looks at how the Sample Average Approximation (SAA) method is used to solve them. We review several applications of this problem-solving technique that have been published in papers over the last few years. The number and variety of the examples should give an indication of the usefulness of this technique. The examples also provide opportunities to discuss important aspects of SPs and the SAA method including model assumptions, optimality gaps, the use of deterministic methods for finite sample sizes, and the accelerated Benders decomposition algorithm. We also give a brief overview of the Sample Approximation (SA) method, and compare it to the SAA method.
Recommended Citation
Fowler, John, "Monte Carlo Approx. Methods for Stochastic Optimization" (2016). Pomona Senior Theses. 242.
https://scholarship.claremont.edu/pomona_theses/242
