Spectral Gaps of Hit-and-Run and Coordinate Hit-and-Run
2026-08-17 • Data Structures and Algorithms
Data Structures and AlgorithmsMachine Learning
AI summaryⓘ
The authors studied how quickly the Hit-and-Run algorithm samples uniformly from any convex shape containing a unit ball. They show that the speed of convergence depends on a mathematical quantity called the Poincaré constant, improving previous bounds based on the shape's size. Their work connects Hit-and-Run’s behavior to important functional inequalities and avoids older methods relying on conductance. They also extend their approach to Coordinate Hit-and-Run, achieving better mixing time estimates. This advances understanding of sampling efficiency with a more general and tighter analysis.
Hit-and-Run algorithmSpectral gapPoincaré constantConvex bodyMixing timeMarkov chainKLS conjectureχ²-divergenceConductanceCoordinate Hit-and-Run
Authors
Yunbum Kook, Santosh S. Vempala
Abstract
For any convex body $\mathcal{K}\subset\mathbb{R}^{n}$ containing a unit ball, the spectral gap of Hit-and-Run is $Ω(1/(n^2 C_{\mathsf{PI}}))$, where $C_{\mathsf{PI}}$ is the Poincaré constant of the uniform distribution $π$ over $\mathcal{K}$. This implies that Hit-and-Run converges to a distribution within $χ^2$-divergence $\varepsilon$ of the uniform distribution $π$ in $O(n^2 C_{\mathsf{PI}}\log(M/\varepsilon))$ steps from any starting distribution $π_0$ with $M=χ^2(π_{0}\,\|\,π)$, thus refining the known bound of $O(n^2 R^2 \log(M/\varepsilon))$ by Lovász and Vempala (2004) in terms of the outer radius $R$; for nearly isotropic bodies, together with progress on the KLS conjecture, the complexity is $O(n^2\log n\log(M/\varepsilon))$, improving the dimension dependence from cubic to nearly quadratic while maintaining logarithmic dependence on the initial distance. It was an open problem to connect the convergence of Hit-and-Run to Poincaré/KLS constants as was done for the Ball walk by Kannan, Lovász and Simonovits (1997). Unlike Hit-and-Run, the Ball walk has an unavoidable linear dependence on (a stronger notion) of the initial warmness. We directly bound the spectral gap of the Hit-and-Run Markov chain by connecting it to functional isoperimetric constants, inspired by the recent analysis of In-and-Out. Rewriting the spectral gap in terms of dual certificates leads to the Babuška--Aziz constant studied in the analysis of PDEs; it is asymptotically bounded by the improved Poincaré constant, which we show can be bounded in terms of the usual Poincaré constant. The proof is based on duality and calculus, unlike known proofs of convergence for Hit-and-Run which are based on bounding the conductance. The same technique can be applied to Coordinate Hit-and-Run, resulting in a much improved mixing time of $O(n^3C_{\mathsf{PI}}\log(M/\varepsilon))$.