I remember using Conjugate Gradient (https://en.wikipedia.org/wiki/Conjugate_gradient_method https://en.wikipedia.org/wiki/Conjugate_gradient_method) for my 4th

I remember using Conjugate Gradient (https://en.wikipedia.org/wiki/Conjugate_gradient_method https://en.wikipedia.org/wiki/Conjugate_gradient_method) for my 4th year university project, which involved finding the steady state of large and complex systems (e.g. railway signalling systems), that were expressed as sparse matrices with millions of rows/columns. It worked really well and executed faster than other iterative methods such as Jacoby/Gauss-Seidel, but the conjugate gradient method was a bit unstable and at times I would not be able to converge toward a solution - I think it was due to the high number of multiplications involved which may cause over-/underflow (but I can't remember). The matrices themselves are a representation of a Markov chain.