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An overview of sparse convex optimization
Dissertation (MSc)--University of Pretoria, 2018.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2018
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| _version_ | 1867613468130017280 |
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| access_status_str | Open Access |
| author2 | Fabris-Rotelli, Inger Nicolette |
| author_browse | Fabris-Rotelli, Inger Nicolette |
| author_facet | Fabris-Rotelli, Inger Nicolette |
| collection | Thesis |
| dc_rights_str_mv | © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2018. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/64352 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:37.472Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/64352 An overview of sparse convex optimization Fabris-Rotelli, Inger Nicolette modiba.jac@gmail.com Modiba, Jacob Mantjitji Statistics UCTD Dissertation (MSc)--University of Pretoria, 2018. Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. Optimization is seeking values of a variable that leads to an optimal value of the function that is to be optimized. Suppose we have a system of equations where there more unknowns than the equations. This type of system leads to an infinitely many solution. If one has prior knowledge that the solution is sparse this problem can be treated as an optimization problem. In this mini-dissertation we will discuss the convex algorithms for finding sparse solution. We use convex algorithm are chosen since they are relatively easy to implement. The class of methods we will discuss are convex relaxation, greedy algorithms and iterative thresholding. We will then compare this algorithms by applying them to a Sudoku problem. CAIR and STATOMET Statistics MSc Unrestricted 2018-03-29T09:23:31Z 2018-03-29T09:23:31Z 2018-09-01 2018-03 Mini Dissertation Modiba, JM 2018, An overview of sparse convex optimization, MSc Mini Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/64352> S2018 http://hdl.handle.net/2263/64352 en © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Statistics UCTD An overview of sparse convex optimization |
| title | An overview of sparse convex optimization |
| title_full | An overview of sparse convex optimization |
| title_fullStr | An overview of sparse convex optimization |
| title_full_unstemmed | An overview of sparse convex optimization |
| title_short | An overview of sparse convex optimization |
| title_sort | overview of sparse convex optimization |
| topic | Statistics UCTD |
| url | http://hdl.handle.net/2263/64352 |