Timeline for Efficient calculation of matrix inverse in R
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
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| Mar 25, 2020 at 11:26 | comment | added | Wolfgang | @Ga13 Not necessarily (and not on two systems that I tested this on). This might depend on your CPU and which linear algebra routines you are using. | |
| Mar 24, 2020 at 22:13 | comment | added | Ga13 | pd.solve is faster! | |
| Mar 6, 2015 at 7:11 | comment | added | Raxel | The Cholesky decomposition is a good choice for covariance/correlation matrices but keep in mind that in general the matrix has to be Hermitian(in case of real matrices that means symmetric), positive definite matrix. That uses half of the memory required for LU decomposition. | |
| Aug 31, 2011 at 19:37 | comment | added | jitendra |
Thanks a lot for this solution. I, at least, know one method that can solve it half the time as compared to solve :-)
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| Aug 31, 2011 at 19:35 | vote | accept | jitendra | ||
| Aug 30, 2011 at 12:37 | history | answered | Wolfgang | CC BY-SA 3.0 |