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Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning./
Author:	Holtz, Chester.
Description:	1 online resource (94 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379759032

Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning.
Holtz, Chester.

Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning. - 1 online resource (94 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2023.

Includes bibliographical references

Recent work has shown that by considering an optimization perspective of the eigenvalues and eigenvectors of graph Laplacians, more efficient algorithms can be developed for tackling many graph-related computing tasks. In this dissertation, we present efficient methods for solving general quadratic programs with nonconvex constraints in the context of very-large-scale integration (VLSI) computer-aided design (CAD) and graph-based semi-supervised learning problems. We propose a general framework for matrix quadratic programming with nonconvex constraints, which is motivated by classic algorithms for solving trust-region subproblems. We introduce approximate and iterative methods with derived convergence guarantees. We demonstrate the effectiveness of our framework on large-scale numerical test cases, specifically real-world benchmarks. By leveraging analytical VLSI and PCB layout engines, we show that effective initialization using our method consistently improves a variety of pre- and post-detailed placement metrics. Additionally, we introduce a graph semi-supervised learning algorithm based on this framework, which yields strong results across a wide spectrum of label rates.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379759032Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Cheng, Chung-Kuan.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2023.
504 $a Includes bibliographical references
520 $a Recent work has shown that by considering an optimization perspective of the eigenvalues and eigenvectors of graph Laplacians, more efficient algorithms can be developed for tackling many graph-related computing tasks. In this dissertation, we present efficient methods for solving general quadratic programs with nonconvex constraints in the context of very-large-scale integration (VLSI) computer-aided design (CAD) and graph-based semi-supervised learning problems. We propose a general framework for matrix quadratic programming with nonconvex constraints, which is motivated by classic algorithms for solving trust-region subproblems. We introduce approximate and iterative methods with derived convergence guarantees. We demonstrate the effectiveness of our framework on large-scale numerical test cases, specifically real-world benchmarks. By leveraging analytical VLSI and PCB layout engines, we show that effective initialization using our method consistently improves a variety of pre- and post-detailed placement metrics. Additionally, we introduce a graph semi-supervised learning algorithm based on this framework, which yields strong results across a wide spectrum of label rates.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Computer engineering. $3 569006
650 4 $a Electrical engineering. $3 596380
653 $a Machine learning
653 $a Optimization
653 $a Graph embedding
653 $a Subspace minimization
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