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Stochastic Optimization and Subgraph Search.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic Optimization and Subgraph Search.
作者:	Moorman, Jacob.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2021
面頁冊數:	183 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
附註:	Advisor: Hunter, Deanna M.;Bertozzi, Andrea.
Contained By:	Dissertations Abstracts International82-12B.
標題:	Applied mathematics.
電子資源:	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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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.
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650 4 $a Applied mathematics. $3 377601
653 $a Kaczmarz method
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