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Randomized Numerical Linear Algebra ...

Dong, Kun.

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Randomized Numerical Linear Algebra for Large-Scale Matrix Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Randomized Numerical Linear Algebra for Large-Scale Matrix Data./
作者:	Dong, Kun.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	176 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Contained By:	Dissertations Abstracts International81-03B.
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22616222
ISBN:	9781088385791

Randomized Numerical Linear Algebra for Large-Scale Matrix Data.
Dong, Kun.

Randomized Numerical Linear Algebra for Large-Scale Matrix Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 176 p.

Source: Dissertations Abstracts International, Volume: 81-03, Section: B.

Thesis (Ph.D.)--Cornell University, 2019.

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

This dissertation is about computational tools based on randomized numerical linear algebra for handling larg-scale matrix data. Since large datasets have become commonly available in a wide variety of modern applications, there has been an increasing demand for numerical methods for storing, processing, and learning from them. Matrices, the classical form for representing datasets, naturally connect these tasks with the rich literature of numerical linear algebra. For a diverse collection of problems, randomized methods offer extraordinary efficiency and flexibility. This work focuses on using randomized numerical linear algebra to build practical algorithms for problems of massive size and high complexity that traditional methods are unable to handle. Through this dissertation, we explore topics across network science, Gaussian process regression, natural language processing, and quantum chemistry. Our contribution includes a collection of scalable and robust numerical methods under a unifying theme, accompanied by efficient implementations. As a result, we are able to significantly speed up the computation for several existing applications, and explore problems and datasets that were intractable before.

ISBN: 9781088385791Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Machine learning

Randomized Numerical Linear Algebra for Large-Scale Matrix Data.
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506 $a This item must not be sold to any third party vendors.
520 $a This dissertation is about computational tools based on randomized numerical linear algebra for handling larg-scale matrix data. Since large datasets have become commonly available in a wide variety of modern applications, there has been an increasing demand for numerical methods for storing, processing, and learning from them. Matrices, the classical form for representing datasets, naturally connect these tasks with the rich literature of numerical linear algebra. For a diverse collection of problems, randomized methods offer extraordinary efficiency and flexibility. This work focuses on using randomized numerical linear algebra to build practical algorithms for problems of massive size and high complexity that traditional methods are unable to handle. Through this dissertation, we explore topics across network science, Gaussian process regression, natural language processing, and quantum chemistry. Our contribution includes a collection of scalable and robust numerical methods under a unifying theme, accompanied by efficient implementations. As a result, we are able to significantly speed up the computation for several existing applications, and explore problems and datasets that were intractable before.
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