I have a generic matrix A which is symmetric, positive definite and sparsely populated (it's also quite big, say composed of tens to hundreds thousands rows). I would like to have a neural network learn how to find a matrix B that resembles the inverse of A (ie: that multiplied by A will yeld a minimal spread of the spectral radius) as accurately and as fast as possible.I was wondering if there are some AI branches that are focused on this kind of problems or if not how could I approach it.I know the classical algorithms used to do this, but I would just like to have a look at the possibilities offered by machine learning on the subject.
Finding the inverse of a matrix with neural networks
machine learning;neural network