Abstract: For expensive multiobjective optimization problems, there exists useful knowledge, e.g., the trained surrogate models, can be transferred to assist the optimization of a target optimization ...
Abstract: This article presents a novel proximal gradient neurodynamic network (PGNN) for solving composite optimization problems (COPs). The proposed PGNN with time-varying coefficients can be ...
NVIDIA's GPU-accelerated cuOpt engine discovers new solutions for four MIPLIB benchmark problems, outperforming CPU solvers with 22% lower objective gaps. NVIDIA's cuOpt optimization engine has found ...
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