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The Lanczos algorithm is an iterative method devised by Cornelius Lanczos that is an adaptation of power methods to find the m {\displaystyle m} "most useful" (tending towards extreme highest/lowest) eigenvalues and eigenvectors of an n × n {\displaystyle n\times n} Hermitian matrix, where m {\displaystyle m} is often but not necessarily much smaller…
Applications & Measurement
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displaystyle lanczos algorithm vector eigenvalues matrix method vectors one since also power iteration eigenvalue eigenvectors dotsc may norm lambda real
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Lanczos algorithm | is a | iterative method devised by Cornelius Lanczos that is an adaptation of power methods to find the m | 0.90 | text |
| restarted Lanczos bidiagonalization | instance of | This has led into a number of other restarted variations | 0.80 | text |
| the HITS algorithm developed by Jon Kleinberg | instance of | Eigenvectors are also important for large-scale ranking methods | 0.80 | text |
| or the PageRank algorithm used by Google.Lanczos algorithms are also used in condensed matter physics as a method for solving Hamiltonians of strongly correlated electron systems | instance of | Eigenvectors are also important for large-scale ranking methods | 0.80 | text |
| as well as in shell model codes in nuclear physics | instance of | Eigenvectors are also important for large-scale ranking methods | 0.80 | text |
| Lanczos algorithm | has application | Lanczos | 0.60 | section |
| Lanczos algorithm | has application | Since | 0.60 | section |
| Lanczos algorithm | has application | Eigenvectors | 0.60 | section |
| Lanczos algorithm | has application | HITS | 0.60 | section |
| Lanczos algorithm | has application | Jon Kleinberg | 0.60 | section |
| Lanczos algorithm | has application | PageRank | 0.60 | section |
| Lanczos algorithm | has application | 0.60 | section |
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