FPRAS Approximation of the Matrix Permanent in Practice

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dc.contributor.advisorVardi, Moshe Yen_US
dc.creatorNewman, Jamesen_US
dc.date.accessioned2020-06-12T15:20:49Zen_US
dc.date.available2020-06-12T15:20:49Zen_US
dc.date.created2020-05en_US
dc.date.issued2020-06-12en_US
dc.date.submittedMay 2020en_US
dc.date.updated2020-06-12T15:20:52Zen_US
dc.description.abstractThe matrix permanent belongs to the complexity class #P-Complete. It is gener- ally believed to be computationally infeasible for large problem sizes, and significant research has been done on approximation algorithms for the matrix permanent. We present an implementation and detailed runtime analysis of one such Markov Chain Monte Carlo (MCMC) based Fully Polynomial Randomized Approximation Scheme (FPRAS) for the matrix permanent which has previously only been described theo- retically and with big-Oh runtime analysis. We demonstrate that the constant factors hidden by the big-Oh analysis result in computational infeasibility. We explore the performance of the FPRAS implementation under relaxed sampling parameters to gauge the room for improvement in the probabilistic analysis of sampling parameter requirements for the FPRAS.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationNewman, James. "FPRAS Approximation of the Matrix Permanent in Practice." (2020) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/108798">https://hdl.handle.net/1911/108798</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/108798en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectFPRASen_US
dc.subjectPermanenten_US
dc.subjectMCMCen_US
dc.subject#Pen_US
dc.subject#P-Completeen_US
dc.titleFPRAS Approximation of the Matrix Permanent in Practiceen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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