A chordal preconditioner for large scale optimization:
We propose an automatic preconditioning scheme for large sparse numerical optimization. The strategy is based on an examination of the sparsity pattern of the Hessian matrix: using a graph-theoretic heuristic, a block diagonal approximation to the Hessian matrix is induced. The blocks are submatrice...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Ithaca, New York
1986
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| Schriftenreihe: | Cornell University <Ithaca, NY> / Department of Computer Science: Technical report
762 |
| Schlagworte: | |
| Zusammenfassung: | We propose an automatic preconditioning scheme for large sparse numerical optimization. The strategy is based on an examination of the sparsity pattern of the Hessian matrix: using a graph-theoretic heuristic, a block diagonal approximation to the Hessian matrix is induced. The blocks are submatrices of the Hessian matrix; furthermore, each block is chordal. That is, under a positive definiteness assumption, each block can be Cholesky factored without creating new nonzeroes (fill). Therefore the preconditioner is space efficient. We conduct a number of numerical experiments to determine the effectiveness of the preconditioner in the context of a linear conjugate gradient algorithm for optimization. |
| Beschreibung: | 41 S. |
Internformat
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| 245 | 1 | 0 | |a A chordal preconditioner for large scale optimization |
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| 490 | 1 | |a Cornell University <Ithaca, NY> / Department of Computer Science: Technical report |v 762 | |
| 520 | 3 | |a We propose an automatic preconditioning scheme for large sparse numerical optimization. The strategy is based on an examination of the sparsity pattern of the Hessian matrix: using a graph-theoretic heuristic, a block diagonal approximation to the Hessian matrix is induced. The blocks are submatrices of the Hessian matrix; furthermore, each block is chordal. That is, under a positive definiteness assumption, each block can be Cholesky factored without creating new nonzeroes (fill). Therefore the preconditioner is space efficient. We conduct a number of numerical experiments to determine the effectiveness of the preconditioner in the context of a linear conjugate gradient algorithm for optimization. | |
| 650 | 4 | |a Heuristic programming | |
| 650 | 4 | |a Mathematical optimization | |
| 810 | 2 | |a Department of Computer Science: Technical report |t Cornell University <Ithaca, NY> |v 762 |w (DE-604)BV006185504 |9 762 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-007065365 | ||
Datensatz im Suchindex
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| author | Coleman, Thomas F. |
| author_facet | Coleman, Thomas F. |
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| author_variant | t f c tf tfc |
| building | Verbundindex |
| bvnumber | BV010594799 |
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| ctrlnum | (OCoLC)15994348 (DE-599)BVBBV010594799 |
| discipline | Mathematik |
| format | Book |
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| id | DE-604.BV010594799 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:55:39Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007065365 |
| oclc_num | 15994348 |
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| physical | 41 S. |
| publishDate | 1986 |
| publishDateSearch | 1986 |
| publishDateSort | 1986 |
| record_format | marc |
| series2 | Cornell University <Ithaca, NY> / Department of Computer Science: Technical report |
| spelling | Coleman, Thomas F. Verfasser aut A chordal preconditioner for large scale optimization Ithaca, New York 1986 41 S. txt rdacontent n rdamedia nc rdacarrier Cornell University <Ithaca, NY> / Department of Computer Science: Technical report 762 We propose an automatic preconditioning scheme for large sparse numerical optimization. The strategy is based on an examination of the sparsity pattern of the Hessian matrix: using a graph-theoretic heuristic, a block diagonal approximation to the Hessian matrix is induced. The blocks are submatrices of the Hessian matrix; furthermore, each block is chordal. That is, under a positive definiteness assumption, each block can be Cholesky factored without creating new nonzeroes (fill). Therefore the preconditioner is space efficient. We conduct a number of numerical experiments to determine the effectiveness of the preconditioner in the context of a linear conjugate gradient algorithm for optimization. Heuristic programming Mathematical optimization Department of Computer Science: Technical report Cornell University <Ithaca, NY> 762 (DE-604)BV006185504 762 |
| spellingShingle | Coleman, Thomas F. A chordal preconditioner for large scale optimization Heuristic programming Mathematical optimization |
| title | A chordal preconditioner for large scale optimization |
| title_auth | A chordal preconditioner for large scale optimization |
| title_exact_search | A chordal preconditioner for large scale optimization |
| title_full | A chordal preconditioner for large scale optimization |
| title_fullStr | A chordal preconditioner for large scale optimization |
| title_full_unstemmed | A chordal preconditioner for large scale optimization |
| title_short | A chordal preconditioner for large scale optimization |
| title_sort | a chordal preconditioner for large scale optimization |
| topic | Heuristic programming Mathematical optimization |
| topic_facet | Heuristic programming Mathematical optimization |
| volume_link | (DE-604)BV006185504 |
| work_keys_str_mv | AT colemanthomasf achordalpreconditionerforlargescaleoptimization |