Computational experiments for local search algorithms for binary and mixed integer optimization
Name
706817731-MIT.pdf
Description
Full printable version
Size
2.25 MB
Format
Adobe PDF
Checksum (MD5)
859db0d6fd3e08c38a857f0737d67d53
Author(s)
Zhou, Jingting, S.M. Massachusetts Institute of Technology
Advisor(s)
Dimitris J. Bertsimas.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also compare our algorithms with CPLEX on large amount of fairly large-size instances. Based on the experimental results, our binary optimization algorithm delivers performance that is strictly better than CPLEX on instances with moderately dense constraint matrices, while for sparse instances, our algorithm delivers performance that is comparable to CPLEX. Our mixed integer optimization algorithm outperforms CPLEX most of the time when the constraint matrices are moderately dense, while for sparse instances, it yields results that are close to CPLEX, and the largest gap relative to the result given by CPLEX is around 5%. Our findings show that these two algorithms, especially the binary optimization algorithm, have practical promise in solving large, dense instances of both set covering and set packing problems.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 53).
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
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