samplewiki

Bounds for a small number of standard sampling problems.

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Choose the assumptions first.

Each page fixes a target class and oracle model, then lists upper bounds, lower bounds, initialization, metric, and caveats in one table.

MarksCheckedpublishedUnverifiedPublication and review are recorded separately.

Strongly log-concave + log-smooth

π∝e−V\pi\propto e^{-V}, αI⪯∇2V⪯βI\alpha I\preceq\nabla^2V\preceq\beta I, κ=β/α\kappa=\beta/\alpha.
Best high-accuracy upperO~(κd1/5 polylog⁡(1/ε))\widetilde O(\kappa d^{1/5}\,\operatorname{polylog}(1/\varepsilon))Gaussian cloud samplerChecked
Best low-accuracy upperO~(κ7/6d1/6/ε1/3)\widetilde O(\kappa^{7/6}d^{1/6}/\varepsilon^{1/3})Smoothed Picard HMC + recursive warm startChecked
Best lowerΩ~(min⁡{κlog⁡d,d})\widetilde\Omega(\min\{\sqrt\kappa\log d,d\})General first-order oracle lower boundChecked

Convex body + membership oracle

π=unif⁡(K)\pi=\operatorname{unif}(K), B(0,1)⊆KB(0,1)\subseteq K, membership queries; Λ=∥Cov⁡(π)∥op\Lambda=\lVert\operatorname{Cov}(\pi)\rVert_{\rm op}.
Best upperO~(qd2Λlog⁡6(1/ε))\widetilde O(qd^2\Lambda\log^6(1/\varepsilon))Proximal / In-and-Out with restartCited
Lower bound unknownUnknownGeneral sampling lower boundUnverified

Log-concave + log-smooth

π∝e−V\pi\propto e^{-V}, 0⪯∇2V⪯βI0\preceq\nabla^2V\preceq\beta I, R02=W22(μ0,π)R_0^2=W_2^2(\mu_0,\pi).
Best upperO~(βd R02/ε2)\widetilde O(\beta\sqrt d\,R_0^2/\varepsilon^2)Implemented proximal samplerChecked
Lower bound unknownUnknownMatching first-order lower boundUnverified

Log-smooth + PI or LSI

∇V\nabla V is β\beta-Lipschitz; CPIC_{\rm PI} or CLSI≤1/αC_{\rm LSI}\le1/\alpha; κ=β/α\kappa=\beta/\alpha.
Best upperO~(κd1/5 polylog⁡(1/ε))\widetilde O(\kappa d^{1/5}\,\operatorname{polylog}(1/\varepsilon))Gaussian cloud samplerChecked
Lower bound unknownUnknownMatching PI/LSI oracle lower boundUnverified

Smooth non-log-concave + Fisher accuracy

∇V\nabla V is β\beta-Lipschitz, K0=KL⁡(μ0∥π)<∞K_0=\operatorname{KL}(\mu_0\Vert\pi)<\infty; target FI⁡≤ε2\operatorname{FI}\le\varepsilon^2.
Best upperO~(βd1/3K0/ε2)\widetilde O(\beta d^{1/3} K_0/\varepsilon^2)Exact ULD / FORSChecked
Best lowerε−2+o(1)\varepsilon^{-2+o(1)}General first-order Fisher lower boundChecked

Stochastic and finite-sum oracles

V=m−1∑i=1mfiV=m^{-1}\sum_{i=1}^m f_i or unbiased noisy gradients; oracle cost counts component or stochastic queries.
Best upperO~((κd+σψ2/α)polylog⁡(1/ε))\widetilde O((\kappa\sqrt d+\sigma_\psi^2/\alpha)\operatorname{polylog}(1/\varepsilon))FORSChecked
Best lowerΩ(σ2/(αε))\Omega(\sigma^2/(\alpha\varepsilon))Bounded-variance stochastic-gradient lower boundChecked
Best finite-sum upperO~((m+κdm)polylog⁡(1/ε))\widetilde O((m+\kappa\sqrt{dm})\operatorname{polylog}(1/\varepsilon))Variance-reduced high-accuracy samplerChecked

Weakly smooth log-concave

VV convex, ∥∇V(x)−∇V(y)∥≤βs∥x−y∥s\lVert\nabla V(x)-\nabla V(y)\rVert\le\beta_s\lVert x-y\rVert^s, s∈[0,1]s\in[0,1].
Best upperO~(βs2/(1+s)ds/(1+s)R02/ε2)\widetilde O(\beta_s^{2/(1+s)}d^{s/(1+s)}R_0^2/\varepsilon^2)FORS + proximal samplerCited
Lower bound unknownUnknownMatching Hölder-model lower boundUnverified