AI RESEARCH
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
arXiv CS.LG
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ArXi:2606.00366v1 Announce Type: new We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems. The goal is for these initial guesses to be high-quality (i.e., a numerical solver converges quickly) and diverse (i.e., represent many different local minima). Identifying multiple locally optimal solutions enables flexible downstream decision-making, but typically requires expensive global search.