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A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems

In this research, we will solve the l0-norm sparse optimisation problem. This is a l0-norm problem with an underdetermined system as its constraint. Using the Lagrangian method, this problem is transformed into an unconstrained optimisation problem. However, it cannot be solved by using the standard...

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Main Author: Woo, Gillian Yi Han
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Q Science (General)
Online Access:http://eprints.utar.edu.my/4585/1/Gillian_Woo_Yi_Han.pdf
http://eprints.utar.edu.my/4585/
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spelling my-utar-eprints.45852022-08-25T13:07:25Z A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems Woo, Gillian Yi Han Q Science (General) In this research, we will solve the l0-norm sparse optimisation problem. This is a l0-norm problem with an underdetermined system as its constraint. Using the Lagrangian method, this problem is transformed into an unconstrained optimisation problem. However, it cannot be solved by using the standard optimisation algorithm since l0-norm is nonconvex and non-smooth. Hence, a new method, the spectral proximal method (SPM) has been proposed and applied to the l0-norm unconstrained optimisation problem. This method is a combination of the proximal method and spectral gradient method. Based on previous research, the performance of the spectral gradient method is better than the other standard unconstrained optimisation methods due to the fact that the approximation of the full rank Hessian matrix is replaced by a diagonal matrix. Hence, the memory required O(n) storage instead of O(n2) storage. Convergent analysis of this method is established. The efficiency of the proposed method with the existing version of proximal gradient methods as its benchmarks are compared using Python software on simulated datasets and also large real-world MNIST datasets. The results show that our proposed method is more robust and stable for finding sparse solution of the linear system. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4585/1/Gillian_Woo_Yi_Han.pdf Woo, Gillian Yi Han (2022) A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems. Master dissertation/thesis, UTAR. http://eprints.utar.edu.my/4585/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic Q Science (General)
spellingShingle Q Science (General)
Woo, Gillian Yi Han
A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems
description In this research, we will solve the l0-norm sparse optimisation problem. This is a l0-norm problem with an underdetermined system as its constraint. Using the Lagrangian method, this problem is transformed into an unconstrained optimisation problem. However, it cannot be solved by using the standard optimisation algorithm since l0-norm is nonconvex and non-smooth. Hence, a new method, the spectral proximal method (SPM) has been proposed and applied to the l0-norm unconstrained optimisation problem. This method is a combination of the proximal method and spectral gradient method. Based on previous research, the performance of the spectral gradient method is better than the other standard unconstrained optimisation methods due to the fact that the approximation of the full rank Hessian matrix is replaced by a diagonal matrix. Hence, the memory required O(n) storage instead of O(n2) storage. Convergent analysis of this method is established. The efficiency of the proposed method with the existing version of proximal gradient methods as its benchmarks are compared using Python software on simulated datasets and also large real-world MNIST datasets. The results show that our proposed method is more robust and stable for finding sparse solution of the linear system.
format Final Year Project / Dissertation / Thesis
author Woo, Gillian Yi Han
author_facet Woo, Gillian Yi Han
author_sort Woo, Gillian Yi Han
title A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems
title_short A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems
title_full A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems
title_fullStr A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems
title_full_unstemmed A Spectral Proximal Methodforsparse Optimisation On Underdetermined Linear Systems
title_sort spectral proximal methodforsparse optimisation on underdetermined linear systems
publishDate 2022
url http://eprints.utar.edu.my/4585/1/Gillian_Woo_Yi_Han.pdf
http://eprints.utar.edu.my/4585/
_version_ 1744358166396665856
score 13.244745

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