iPULA replaces exact proximal steps with inexact approximations in unadjusted Langevin sampling and proves non-asymptotic convergence that holds up to a quantifiable bias from the inexactness.
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Pith papers citing it
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces HOME-DC smoothing for DC functions, derives an inexact first-order oracle, and proposes convergent inexact descent methods with preliminary numerical support on sparse clustering.
citing papers explorer
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Speeding Up Nonsmooth Bayesian MCMC Sampling via Inexact Proximal Unadjusted Langevin Algorithm
iPULA replaces exact proximal steps with inexact approximations in unadjusted Langevin sampling and proves non-asymptotic convergence that holds up to a quantifiable bias from the inexactness.
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Difference-of-Convex Optimization via Inexact Smoothing Descent Methods: Difference of High-Order Moreau Envelopes
Introduces HOME-DC smoothing for DC functions, derives an inexact first-order oracle, and proposes convergent inexact descent methods with preliminary numerical support on sparse clustering.