Global Optimization via Softmin Energy Minimization

ArXi:2509.17815v2 Announce Type: replace Global optimization, particularly for non-convex functions with multiple local minima, poses significant challenges for traditional gradient-based methods. While metaheuristic approaches offer empirical effectiveness, they often lack theoretical convergence guarantees and may disregard available gradient information. This paper