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Double-Loop Unadjusted Langevin Algorithm

ICML 2020 · Optimization › Nonconvex and smooth optimization · approach: Langevin sampling for nonconvex problems · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a double-loop geometrically decaying step-size schedule instead of standard ULAULA · Smooth unconstrained log-concave samplingiteration complexity (KL): \wt{O}\l(Ld^3\epsilon^{-2} \r) -> \tilde{\mathcal{O}}(Ld^3\epsilon^{-\frac{3}{2}})Helped
Use a double-loop geometrically decaying step-size schedule instead of the cited TV baselineULA · Smooth unconstrained log-concave samplingiteration complexity (TV): \wt{O} \l(Ld^3\epsilon^{-4} \r) -> \wt{\mathcal{O}}(Ld^3\epsilon^{-3})Mixed
Use a double-loop schedule and projectionULA · Smooth unconstrained log-concave samplingiteration complexity (W2): \text{no prior result in Table 1} -> \tilde{\mathcal{O}}(Ld^9\epsilon^{-6})Helped
Apply the double-loop schedule and decrease the penalty parameter across outer loopsMYULA · Constrained log-concave samplingiteration complexity (TV): d^{5} \wt{O} \l(\epsilon^{-6} \r) -> \mathcal{O}\left(d^{3.5} \epsilon^{-5} \right)Helped
Decrease the penalty parameter across outer loopsMYULA · Constrained log-concave samplingasymptotic convergenceHelped
Add a projection step after each outer loopDL-ULA · Smooth unconstrained log-concave samplingtail control for W2 analysisHelped

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