Topics in convex optimization for machine learning (Book, 2020) [WorldCat.org]
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Topics in convex optimization for machine learning

Author: Youngsuk Park; Stephen P Boyd; Jurij Leskovec; Tsachy Weissman; Stanford University. Department of Electrical Engineering.
Publisher: [Stanford, California] : [Stanford University], 2020. ©2020
Dissertation: Ph.D. Stanford University 2020. Thesis
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Convex optimization has been well-studied as a mathematical topic for more than a century, and has been applied in practice in many application areas for about a half century in fields including control, finance, signal processing, data mining, and machine learning. This thesis focuses on several topics involving convex optimization, with the specific application of machine learning.
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Details

Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Youngsuk Park; Stephen P Boyd; Jurij Leskovec; Tsachy Weissman; Stanford University. Department of Electrical Engineering.
OCLC Number: 1151960100
Notes: Submitted to the Department of Electrical Engineering.
Description: 1 online resource
Responsibility: Youngsuk Park.

Abstract:

Convex optimization has been well-studied as a mathematical topic for more than a century, and has been applied in practice in many application areas for about a half century in fields including control, finance, signal processing, data mining, and machine learning. This thesis focuses on several topics involving convex optimization, with the specific application of machine learning.

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