Derives the cold Sinkhorn limiting dynamics as tau approaches zero, proving finite-time convergence to unregularized OT and improved O(tau^{-1}) iteration complexity for dual suboptimality.
Diagonal linear networks and the lasso regularization path
2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Rescaled mirror flows converge to a limit whose primal variable incrementally minimizes quadratic loss over a subdifferential-defined time-dependent hypothesis set.
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Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization
Derives the cold Sinkhorn limiting dynamics as tau approaches zero, proving finite-time convergence to unregularized OT and improved O(tau^{-1}) iteration complexity for dual suboptimality.
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Incremental Learning in Mirror Flows
Rescaled mirror flows converge to a limit whose primal variable incrementally minimizes quadratic loss over a subdifferential-defined time-dependent hypothesis set.