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Stochastic Optimization and Subgraph Search.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic Optimization and Subgraph Search.
Author:	Moorman, Jacob.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2021
Description:	183 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Notes:	Advisor: Hunter, Deanna M.;Bertozzi, Andrea.
Contained By:	Dissertations Abstracts International82-12B.
Subject:	Applied mathematics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28546255
ISBN:	9798516926853

Stochastic Optimization and Subgraph Search.
Moorman, Jacob.

Stochastic Optimization and Subgraph Search. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 183 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2021.

This item must not be sold to any third party vendors.

In this thesis, we study randomized algorithms for numerical linear algebra and pattern matching algorithms for multiplex networks. We first analyze the convergence of two classes of randomized iterative methods for solving large linear systems of equations. In particular, we analyze sketch-and-project methods with adaptive sampling strategies and parallelized randomized Kaczmarz methods with averaging. We observe empirically that the convergence of these methods reflects the worst-case convergence theory. We later discuss subgraph matching and various related problems including inexact search. We introduce filtering algorithms that are specialized to multiplex networks. In both the exact and inexact settings, we aim to understand the entire solution space rather than simply finding one match for a given pattern. We observe that there are often a combinatorially large number of matches depending on the amount of symmetry in the pattern and data.

ISBN: 9798516926853Subjects--Topical Terms:

377601
Applied mathematics.
Subjects--Index Terms:

Kaczmarz method

Stochastic Optimization and Subgraph Search.
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245 1 0 $a Stochastic Optimization and Subgraph Search.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 183 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
500 $a Advisor: Hunter, Deanna M.;Bertozzi, Andrea.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In this thesis, we study randomized algorithms for numerical linear algebra and pattern matching algorithms for multiplex networks. We first analyze the convergence of two classes of randomized iterative methods for solving large linear systems of equations. In particular, we analyze sketch-and-project methods with adaptive sampling strategies and parallelized randomized Kaczmarz methods with averaging. We observe empirically that the convergence of these methods reflects the worst-case convergence theory. We later discuss subgraph matching and various related problems including inexact search. We introduce filtering algorithms that are specialized to multiplex networks. In both the exact and inexact settings, we aim to understand the entire solution space rather than simply finding one match for a given pattern. We observe that there are often a combinatorially large number of matches depending on the amount of symmetry in the pattern and data.
590 $a School code: 0031.
650 4 $a Applied mathematics. $3 377601
653 $a Kaczmarz method
690 $a 0364
710 2 $a University of California, Los Angeles. $b Mathematics 0540. $3 708654
773 0 $t Dissertations Abstracts International $g 82-12B.
790 $a 0031
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28546255