Revision 5726dd467778eb2f2fd1caec8909609230aa8444 authored by Mark van der Wilk on 09 August 2016, 18:39:33 UTC, committed by James Hensman on 09 August 2016, 18:39:33 UTC
GPflow often optimizes positive-definite matrices. To maintain positive-definiteness without constrained optimization, a lower-triangular matrix is optimized. Sigma + L L ^T The previous approach to optimizing L was to ignore the upper half. The mean that there were some extra variables in the optimization vector, which did nothing. This PR implements a tensorflow op which transforms back-and-forth between triangular matrix L and a 'packed' vector representation. The result is that there are no redundant parameters in the optimization vector.
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GPflow | ||
notebooks | ||
testing | ||
.coveragerc | -rw-r--r-- | 251 bytes |
.coveralls.yml | -rw-r--r-- | 23 bytes |
.gitignore | -rw-r--r-- | 795 bytes |
LICENSE | -rw-r--r-- | 11.1 KB |
README.md | -rw-r--r-- | 5.0 KB |
RELEASE.md | -rw-r--r-- | 1.8 KB |
regression.ipynb | -rw-r--r-- | 206.1 KB |
setup.py | -rw-r--r-- | 2.0 KB |
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