https://github.com/GPflow/GPflow
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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- 5726dd467778eb2f2fd1caec8909609230aa8444
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Fix covariance overparameterisation (#150)
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