Traditional techniques for non-convex problems involve compromises.

local optimization: find a point that minimize \(f_{0}\) among feasible points near it; can handle large problems (i.e. neural networks); algorithm parameter tuning.

global optimization methods: basically just cast it into a convex optimization problem.