dc.contributor.advisor |
Fabris-Rotelli, Inger Nicolette |
|
dc.contributor.postgraduate |
Modiba, Jacob Mantjitji |
|
dc.date.accessioned |
2018-03-29T09:23:31Z |
|
dc.date.available |
2018-03-29T09:23:31Z |
|
dc.date.created |
2018-09-01 |
|
dc.date.issued |
2018-03 |
|
dc.description |
Dissertation (MSc)--University of Pretoria, 2018. |
en_ZA |
dc.description.abstract |
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. |
en_ZA |
dc.description.availability |
Unrestricted |
en_ZA |
dc.description.degree |
MSc |
en_ZA |
dc.description.department |
Statistics |
en_ZA |
dc.description.sponsorship |
CAIR and STATOMET |
en_ZA |
dc.identifier.citation |
Modiba, JM 2018, An overview of sparse convex optimization, MSc Mini Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/64352> |
en_ZA |
dc.identifier.other |
S2018 |
en_ZA |
dc.identifier.uri |
http://hdl.handle.net/2263/64352 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
University of Pretoria |
|
dc.rights |
© 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. |
|
dc.subject |
Statistics |
en_ZA |
dc.subject |
UCTD |
|
dc.title |
An overview of sparse convex optimization |
en_ZA |
dc.type |
Mini Dissertation |
en_ZA |