Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning- [electronic resource]
Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100115
- ISBN
- 9798379759032
- DDC
- 004
- 저자명
- Holtz, Chester.
- 서명/저자
- Graph Embedding via Subspace Minimization with Applications to Chip Placement and Semi-Supervised Learning - [electronic resource]
- 발행사항
- [S.l.]: : University of California, San Diego., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(94 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Cheng, Chung-Kuan.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Recent 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.
- 일반주제명
- Computer science.
- 일반주제명
- Computer engineering.
- 일반주제명
- Electrical engineering.
- 키워드
- Machine learning
- 키워드
- Optimization
- 키워드
- Graph embedding
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.