Apple proposes semismooth Newton solver for kernel-based optimal transport
Read original source →machinelearning.apple.com
The method recasts kernel-based OT as a nonsmooth fixed-point problem, cutting per-iteration cost versus the short-step interior-point method (SSIPM). It proves O(1/√k) global convergence, local quadratic convergence under regularity conditions, and delivers substantial speedups over SSIPM on synthetic and real datasets.
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