A preconditioned regularized Wasserstein proximal sampling algorithm is introduced for particle-based approximation of Gibbs distributions, featuring a PDE-derived kernel formulation and non-asymptotic convergence analysis for quadratic potentials.
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2025 2verdicts
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Approximates large matrix multiplication via truncated SVD and circulant decompositions with O(n^2 log n) complexity and ~1% relative error, including LLM operation demonstrations.
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Preconditioned Regularized Wasserstein Proximal Sampling
A preconditioned regularized Wasserstein proximal sampling algorithm is introduced for particle-based approximation of Gibbs distributions, featuring a PDE-derived kernel formulation and non-asymptotic convergence analysis for quadratic potentials.
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Efficient approximations of matrix multiplication using truncated decompositions
Approximates large matrix multiplication via truncated SVD and circulant decompositions with O(n^2 log n) complexity and ~1% relative error, including LLM operation demonstrations.