CHISAO recovers all modes on the full SFU benchmark suite up to dimension 64 with 100% success using GPU parallelism and a convergence-anticonvergence oscillation, where CPU baselines fail at d >= 8.
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Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
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\chisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation
CHISAO recovers all modes on the full SFU benchmark suite up to dimension 64 with 100% success using GPU parallelism and a convergence-anticonvergence oscillation, where CPU baselines fail at d >= 8.
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.