Nonlinear preconditioning extends Newton methods to objectives lacking Hessian Lipschitz continuity by analyzing a transformed mapping under a relaxed smoothness condition, with superlinear convergence and O(ε^{-3/2}) iteration complexity.
Forward-backward splitting under the light of generalized convexity
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Lifts CCCP to Wasserstein space for DC functionals on measures, proves almost stationarity under smoothness/strong-convexity assumptions, and applies to MMD/ED with local convergence and faster empirical runs.
citing papers explorer
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Newton methods beyond Hessian Lipschitz continuity: A nonlinear preconditioning approach
Nonlinear preconditioning extends Newton methods to objectives lacking Hessian Lipschitz continuity by analyzing a transformed mapping under a relaxed smoothness condition, with superlinear convergence and O(ε^{-3/2}) iteration complexity.
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Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization
Lifts CCCP to Wasserstein space for DC functionals on measures, proves almost stationarity under smoothness/strong-convexity assumptions, and applies to MMD/ED with local convergence and faster empirical runs.