Presentation | 2007-12-13 Comparison of Eigenvalue Computation Speed for Time-Varying Large-Size Symmetric Matrices Hiroyuki TAIRA, Kenichi KANATANI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We study iterative schemes for computing the largest eigenvalue and the corresponding eigenvector of a time-varying large-scale symmetric matrix, which frequently arises in many problems including computer vision. Here, we focus on the steepest descent, the conjugate gradient, the power method, and its acceleration. We first summarize their algorithms and then compare their computation time, using simulated data. We show how the computational efficiency of individual methods is related to the eigenvalue distribution of the matrix in question. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | large-scale matrix / eigenvalue problem / steepest descent / conjugate gradient method / power method / acceleration of convergence |
Paper # | PRMU2007-135 |
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Committee | PRMU |
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Conference Date | 2007/12/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Comparison of Eigenvalue Computation Speed for Time-Varying Large-Size Symmetric Matrices |
Sub Title (in English) | |
Keyword(1) | large-scale matrix |
Keyword(2) | eigenvalue problem |
Keyword(3) | steepest descent |
Keyword(4) | conjugate gradient method |
Keyword(5) | power method |
Keyword(6) | acceleration of convergence |
1st Author's Name | Hiroyuki TAIRA |
1st Author's Affiliation | Department of Computer Science, Okayama University() |
2nd Author's Name | Kenichi KANATANI |
2nd Author's Affiliation | Department of Computer Science, Okayama University |
Date | 2007-12-13 |
Paper # | PRMU2007-135 |
Volume (vol) | vol.107 |
Number (no) | 384 |
Page | pp.pp.- |
#Pages | 6 |
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