On two proofs of $d^2$ mixing of weighted Dikin walks
- URL: http://arxiv.org/abs/2608.28566v1
- Date: Fri, 28 Aug 2026 17:43:58 GMT
- Title: On two proofs of $d^2$ mixing of weighted Dikin walks
- Abstract summary: We study the mixing time of weighted Dikin walks for sampling from exponential distributions on polytopes and truncated positive-semidefinite (PSD) cones.<n>Applying this framework to the Lee--Sidford, Lewis-weight, and John metrics yields an $widetilde O(d4)$ mixing bound for sampling from polytopes.
- Score: 6.216428054339837
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the mixing time of weighted Dikin walks for sampling from exponential distributions on polytopes and truncated positive-semidefinite (PSD) cones. Our first result gives a general total-variation mixing bound under strong self-concordance, $\barν$-symmetry, and mixed-trace regularity on the local metric. The key idea is to control the Metropolis--Hastings acceptance probability on a high-probability region rather than at every point. Applying this framework to the Lee--Sidford, Lewis-weight, and John metrics yields an $\widetilde O(d^2)$ mixing bound for sampling from polytopes, while applying it to a hybrid barrier yields an $\widetilde O(d^4)$ mixing bound for sampling from truncated PSD cones. Our second result establishes stronger $χ^2$-divergence guarantees and pointwise acceptance control using a new fourth-order bootstrap condition. For a suitably scaled Lee--Sidford metric, this yields an $\widetilde O(d^2)$ mixing bound in $χ^2$-divergence, improving on the previous $\widetilde O(d^{9/4})$ bound.
Related papers
- A General Framework for Metropolis-Adjusted Dikin Walks: Dimension-Square Mixing on Polytopes and Log-Det Walks on Spectrahedra [12.89814955033651]
We analyze exact-metric, Metropolis-adjusted Dikin walks by keeping the proposal determinant and reverse quadratic form together.<n>For a polytope given by $n$ inequalities and a $L$-Lipschitz potential, this yields warm-start mixing in $widetilde O((d2+dL2R2)log(w/)$ steps for the regularized Lee--Sidford walk.
arXiv Detail & Related papers (2026-08-26T01:18:00Z) - Beyond the $d^{2.5}$-mixing bound for Dikin walks on polytopes [5.744814240870601]
In 2009, Kannan and Narayanan introduced the Dikin walk for sampling uniformly from polytopes.<n>In 2017, Chen, Dwivedi, Wainwright, and Yu improved this to $d2.5$ using a Lewis-weight barrier.<n>For exponential sampling over a polytope, we prove that the Dikin walk with a scaled Lee--Sidford metric mixes from a warm start in $d2.25$ iterations.
arXiv Detail & Related papers (2026-07-15T15:29:25Z) - High-Probability PL-SGD with Markovian Noise: Optimal Mixing and Tail Dependence [41.64418624570687]
We study first-order methods for objectives satisfying the Polyak-ojasiewicz (PL) condition when samples are generated by an Markov chain.<n>In the lighttailed setting, prior uniform-in-time high-probability bounds for ordinary Gradient Descent (SGD) under a standard growth envelope scale as $widetildeO(t_mix/k)$.<n>We prove this linear dependence on the mixing time is optimal via a matching $(2 t_mix/k)$ lower bound on a quadratic objective driven by a persistent two
arXiv Detail & Related papers (2026-06-24T19:00:56Z) - Optimal Dimension-Free Sampling for Regularized Classification [56.72526267755301]
We prove optimal sampling bounds achieving $(1pmvarepsilon)$-relative error for a broad class of Lipschitz continuous classification loss functions.<n>This includes important functions such as logistic and sigmoid loss, hinge loss, and ReLU loss, as prominent and popular representative examples.
arXiv Detail & Related papers (2026-05-22T15:05:33Z) - Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness [74.18546828528298]
We show that SignSGD with Majority Voting can robustly work on the whole range of complexity with $kappakappakappakappa-1right, kappakappakappa-1right, kappakappakappa-1right, kappakappakappa-1right, kappakappakappa-1right, kappakappakappa-1right, kappakappakappa-1right, kappa
arXiv Detail & Related papers (2025-02-11T19:54:11Z) - Regularized Dikin Walks for Sampling Truncated Logconcave Measures, Mixed Isoperimetry and Beyond Worst-Case Analysis [3.399289369740637]
We study the problem of drawing samples from a logconcave distribution truncated on a polytope.<n>Building on interior point methods and the Dikin walk, we analyze the mixing time of regularized Dikin walks.
arXiv Detail & Related papers (2024-12-15T20:43:51Z) - Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees [56.80920351680438]
We study high-probability convergence in online learning, in the presence of heavy-tailed noise.<n>We provide guarantees for a broad class of nonlinearities, without any assumptions on noise moments.
arXiv Detail & Related papers (2024-10-17T18:25:28Z) - Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Near Sample-Optimal Reduction-based Policy Learning for Average Reward
MDP [58.13930707612128]
This work considers the sample complexity of obtaining an $varepsilon$-optimal policy in an average reward Markov Decision Process (AMDP)
We prove an upper bound of $widetilde O(H varepsilon-3 ln frac1delta)$ samples per state-action pair, where $H := sp(h*)$ is the span of bias of any optimal policy, $varepsilon$ is the accuracy and $delta$ is the failure probability.
arXiv Detail & Related papers (2022-12-01T15:57:58Z) - A Law of Robustness beyond Isoperimetry [84.33752026418045]
We prove a Lipschitzness lower bound $Omega(sqrtn/p)$ of robustness of interpolating neural network parameters on arbitrary distributions.
We then show the potential benefit of overparametrization for smooth data when $n=mathrmpoly(d)$.
We disprove the potential existence of an $O(1)$-Lipschitz robust interpolating function when $n=exp(omega(d))$.
arXiv Detail & Related papers (2022-02-23T16:10:23Z) - Optimal Robust Linear Regression in Nearly Linear Time [97.11565882347772]
We study the problem of high-dimensional robust linear regression where a learner is given access to $n$ samples from the generative model $Y = langle X,w* rangle + epsilon$
We propose estimators for this problem under two settings: (i) $X$ is L4-L2 hypercontractive, $mathbbE [XXtop]$ has bounded condition number and $epsilon$ has bounded variance and (ii) $X$ is sub-Gaussian with identity second moment and $epsilon$ is
arXiv Detail & Related papers (2020-07-16T06:44:44Z) - Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and
Variance Reduction [63.41789556777387]
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP)
We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
arXiv Detail & Related papers (2020-06-04T17:51:00Z) - Curse of Dimensionality on Randomized Smoothing for Certifiable
Robustness [151.67113334248464]
We show that extending the smoothing technique to defend against other attack models can be challenging.
We present experimental results on CIFAR to validate our theory.
arXiv Detail & Related papers (2020-02-08T22:02:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.