  We propose a new algorithm for efficiently solving the damped Fisher matrix
in large-scale scenarios where the number of parameters significantly exceeds
the number of available samples. This problem is fundamental for natural
gradient descent and stochastic reconfiguration. Our algorithm is based on
Cholesky decomposition and is generally applicable. Benchmark results show that
the algorithm is significantly faster than existing methods.
