Abstract: Traditional ant colony algorithms suffer from shortcomings such as blind initial search, slow convergence, and susceptibility to local optima when addressing path planning problems. To ...
Ant colony optimization (ACO) algorithms [1], inspired by the cooperative foraging behavior of ants, are praised for their distributed intelligence, parallelism, positive feedback, and robustness.
turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...
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