Parallel Sparse Optimization

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dc.contributor.advisorYin, Wotaoen_US
dc.contributor.committeeMemberZhang, Yinen_US
dc.contributor.committeeMemberBaraniuk, Richard G.en_US
dc.creatorPeng, Zhiminen_US
dc.date.accessioned2014-10-08T14:57:12Zen_US
dc.date.available2014-10-08T14:57:12Zen_US
dc.date.created2013-12en_US
dc.date.issued2013-08-27en_US
dc.date.submittedDecember 2013en_US
dc.date.updated2014-10-08T14:57:12Zen_US
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.en_US
dc.format.mimetypeapplication/pdfen_US
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>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/77447en_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.subjectSparse optimizationen_US
dc.subjectParallel computingen_US
dc.subjectDistributed computingen_US
dc.subjectProx-linear methodsen_US
dc.subjectGrocken_US
dc.subjectApplied MathSparse optimizationen_US
dc.subjectParallel computingen_US
dc.subjectDistributed computingen_US
dc.subjectProx-linear methodsen_US
dc.subjectGrocken_US
dc.subjectApplied MathSparse optimizationen_US
dc.subjectParallel computingen_US
dc.subjectDistributed computingen_US
dc.subjectProx-linear methodsen_US
dc.subjectGrocken_US
dc.subjectApplied MathSparse optimizationen_US
dc.subjectParallel computingen_US
dc.subjectDistributed computingen_US
dc.subjectProx-linear methodsen_US
dc.subjectGrocken_US
dc.subjectApplied MathSparse optimizationen_US
dc.subjectParallel computingen_US
dc.subjectDistributed computingen_US
dc.subjectProx-linear methodsen_US
dc.subjectGrocken_US
dc.subjectApplied MathSparse optimizationen_US
dc.subjectParallel computingen_US
dc.subjectDistributed computingen_US
dc.subjectProx-linear methodsen_US
dc.subjectGrocken_US
dc.subjectApplied Mathen_US
dc.titleParallel Sparse Optimizationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputational and Applied Mathematicsen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Artsen_US
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