FPRAS Approximation of the Matrix Permanent in Practice

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dc.contributor.advisorVardi, Moshe Y
dc.creatorNewman, James
dc.date.accessioned2020-06-12T15:20:49Z
dc.date.available2020-06-12T15:20:49Z
dc.date.created2020-05
dc.date.issued2020-06-12
dc.date.submittedMay 2020
dc.date.updated2020-06-12T15:20:52Z
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.
dc.format.mimetypeapplication/pdf
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>.
dc.identifier.urihttps://hdl.handle.net/1911/108798
dc.language.isoeng
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.
dc.subjectFPRAS
dc.subjectPermanent
dc.subjectMCMC
dc.subject#P
dc.subject#P-Complete
dc.titleFPRAS Approximation of the Matrix Permanent in Practice
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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