Parallel Sparse Optimization

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dc.contributor.advisorYin, Wotao
dc.contributor.committeeMemberZhang, Yin
dc.contributor.committeeMemberBaraniuk, Richard G.
dc.creatorPeng, Zhimin
dc.date.accessioned2014-10-08T14:57:12Z
dc.date.available2014-10-08T14:57:12Z
dc.date.created2013-12
dc.date.issued2013-08-27
dc.date.submittedDecember 2013
dc.date.updated2014-10-08T14:57:12Z
dc.description.abstractThis thesis proposes parallel and distributed algorithms for solving very largescale sparse optimization problems on computer clusters and clouds. Many modern applications problems from compressive sensing, machine learning and signal and image processing involve large-scale data and can be modeled as sparse optimization problems. Those problems are in such a large-scale that they can no longer be processed on single workstations running single-threaded computing approaches. Moving to parallel/distributed/cloud computing becomes a viable option. I propose two approaches for solving these problems. The first approach is the distributed implementations of a class of efficient proximal linear methods for solving convex optimization problems by taking advantages of the separability of the terms in the objective. The second approach is a parallel greedy coordinate descent method (GRock), which greedily choose several entries to update in parallel in each iteration. I establish the convergence of GRock and explain why it often performs exceptionally well for sparse optimization. Extensive numerical results on a computer cluster and Amazon EC2 demonstrate the efficiency and elasticity of my algorithms.
dc.format.mimetypeapplication/pdf
dc.identifier.citationPeng, Zhimin. "Parallel Sparse Optimization." (2013) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/77447">https://hdl.handle.net/1911/77447</a>.
dc.identifier.urihttps://hdl.handle.net/1911/77447
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.subjectSparse optimization
dc.subjectParallel computing
dc.subjectDistributed computing
dc.subjectProx-linear methods
dc.subjectGrock
dc.subjectApplied MathSparse optimization
dc.subjectParallel computing
dc.subjectDistributed computing
dc.subjectProx-linear methods
dc.subjectGrock
dc.subjectApplied MathSparse optimization
dc.subjectParallel computing
dc.subjectDistributed computing
dc.subjectProx-linear methods
dc.subjectGrock
dc.subjectApplied MathSparse optimization
dc.subjectParallel computing
dc.subjectDistributed computing
dc.subjectProx-linear methods
dc.subjectGrock
dc.subjectApplied MathSparse optimization
dc.subjectParallel computing
dc.subjectDistributed computing
dc.subjectProx-linear methods
dc.subjectGrock
dc.subjectApplied MathSparse optimization
dc.subjectParallel computing
dc.subjectDistributed computing
dc.subjectProx-linear methods
dc.subjectGrock
dc.subjectApplied Math
dc.titleParallel Sparse Optimization
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputational and Applied Mathematics
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Arts
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