I’m encountering challenges while simulating an algorithm. I’m working to optimize a convex function (theoretically convex since it originates from -2log(p(X; Y)), relating to a Maximum Likelihood Estimation problem), where the variable Y is an m*m symmetric positive definite matrix.
Using scipy.optimize, I can successfully optimize the function in one-dimensional and two-dimensional cases. However, in three dimensions, optimization only works with small data sets; larger sets fail. I switched to using cvxpy, but even for two-dimensional systems, it errors out with “You are trying to minimize a function that is concave.”
Could anyone suggest alternative methods or approaches for this simulation?