Complexity of convex optimization using geometry-based measures and a reference point
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Complexity of convex optimization using geometry-based measures and a reference point
- Publication date
- 2001
- Publisher
- [Cambridge, Mass. : Alfred P. Sloan School of Management, Massachusetts Institute of Technology
- Collection
- mitlibraries; blc; americana
- Contributor
- MIT Libraries
- Language
- English
Our concern lies in solving the following convex optimization problem: minimize cx subject to Ax=b, x \in P, where P is a closed convex set. We bound the complexity of computing an almost-optimal solution of this problem in terms of natural geometry-based measures of the feasible region and the level-set of almost-optimal solutions, relative to a given reference point xr that might be close to the feasible region and/or the almost-optimal level set. This contrasts with other complexity bounds for convex optimization that rely on data-based condition numbers or algebraic measures, and that do not take into account any a priori reference point information. Keywords: Convex Optimization, Complexity, Interior-Point Method, Barrier Method
Includes bibliographical references (leaf 29)
Our concern lies in solving the following convex optimization problem: minimize cx subject to Ax=b, x \in P, where P is a closed convex set. We bound the complexity of computing an almost-optimal solution of this problem in terms of natural geometry-based measures of the feasible region and the level-set of almost-optimal solutions, relative to a given reference point xr that might be close to the feasible region and/or the almost-optimal level set. This contrasts with other complexity bounds for convex optimization that rely on data-based condition numbers or algebraic measures, and that do not take into account any a priori reference point information. Keywords: Convex Optimization, Complexity, Interior-Point Method, Barrier Method
Includes bibliographical references (leaf 29)
Our concern lies in solving the following convex optimization problem: minimize cx subject to Ax=b, x \in P, where P is a closed convex set. We bound the complexity of computing an almost-optimal solution of this problem in terms of natural geometry-based measures of the feasible region and the level-set of almost-optimal solutions, relative to a given reference point xr that might be close to the feasible region and/or the almost-optimal level set. This contrasts with other complexity bounds for convex optimization that rely on data-based condition numbers or algebraic measures, and that do not take into account any a priori reference point information. Keywords: Convex Optimization, Complexity, Interior-Point Method, Barrier Method
- Addeddate
- 2008-09-25 19:21:45
- Associated-names
- Sloan School of Management
- Call number
- 001118098
- Camera
- Canon 5D
- External-identifier
- urn:oclc:record:1042412421
- Foldoutcount
- 0
- Identifier
- complexityofconv00freu
- Identifier-ark
- ark:/13960/t9d50vq1z
- Ocr_converted
- abbyy-to-hocr 1.1.37
- Ocr_module_version
- 0.0.21
- Openlibrary_edition
- OL17936577M
- Openlibrary_work
- OL10719380W
- Page_number_confidence
- 18
- Page_number_module_version
- 1.0.3
- Pages
- 74
- Possible copyright status
- NOT_IN_COPYRIGHT
- Ppi
- 300
- Scandate
- 20080926184838
- Scanfactors
- 0
- Scanner
- scribe5.boston.archive.org
- Scanningcenter
- boston
- Worldcat (source edition)
- 50876146
- Full catalog record
- MARCXML
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