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Justin Salez

Publications and source records attributed to Justin Salez.

At least 19 recordsLinked to original sources

Cutoff for random walks on nilpotent groups

We study continuous-time random walks on finite nilpotent groups, allowing non-symmetric and nonuniform increment measures. Under a simple degree condition, sharp up to an absolute constant factor, we prove that total variation cutoff occurs if and only if the relaxation time is of smaller order than the mixing time. We establish a quantitative reduction to the Abelianization that preserves relaxation times and compares mixing profiles. Combining this reduction with a Gaussian approximation and the third-named author's cutoff criterion for diffusions yields the cutoff characterization.

math.PR↗

Cutoff beyond non-negative curvature

We study how a relaxed gradient estimate is enough to prove cutoff for several new families of Markov processes, within the varentropy framework of [Sal25]. In particular, we show how certain regularization estimates from hypocoercivity theory allow one to prove cutoff for kinetic dynamics. Moreover, we prove cutoff for some families of diffusions with non-convex potentials and for mean-field dynamics, as well as for biased random walks on the symmetric group, giving a simple proof of a far-reaching extension of a result of [NY24].

math.PR↗

Universality of cutoff for the Exclusion Process

Under a fairly general condition on the underlying jump rates, we prove that the Exclusion Process with fixed particle density exhibits cutoff at time $t_{mix}\sim \frac{1}{2}{t}_{rel} \log N$, where $N$ is the volume and ${t}_{rel}$ the relaxation time of the single-particle dynamics. Our result covers, in particular, the standard setting of uniform nearest-neighbor jumps on large discrete tori in any fixed dimension and, more generally, on any sequence of vertex-transitive graphs with bounded degree and polynomially diverging diameter. Our (short and entirely human) proof combines Wilson's method, the Octopus Inequality, Fourier analysis on Hamming slices, and a sharp comparison with the Dirichlet form of a natural accelerated variant of the dynamics.

math.PR↗

The local product condition implies cutoff

In the theory of mixing times, a famously wrong conjecture predicts that a sequence of Markov processes exhibits cutoff as soon as the product of their Poincaré constant and mixing time diverges. We prove that this statement becomes correct once the Poincaré constant $γ$ is replaced with its natural non-equilibrium refinement, which we denote by $γ_\star$. More precisely, we show that the width of the mixing window of any Markov process is $O(1/γ_\star)$. This estimate is sharp, and universal up to standard regularity assumptions: it holds on finite and infinite state spaces and from any initial condition, and it does not require reversibility, nor any kind of a chain rule. In addition, for deterministic initialization we show that $γ_\star\geκ$, where $κ$ is the Bakry-Émery curvature, making our result broadly applicable. Finally, our proof is short and self-contained: we simply follow the classical idea of replacing the total variation distance by the more tractable $χ^2$-divergence, but with the crucial novelty that the reference measure evolves in time, instead of being the equilibrium law.

math.PR↗

How fast does the range of simple random walk grow?

Consider a discrete-time simple random walk $(X_t)_{t\ge 0}$ on an infinite, connected, locally finite simple graph $G$, and let \[ R_t := |\{X_0,\ldots,X_t\}| \] denote its range. The main result of this revised note is that positive vertex isoperimetry already forces linear expected range, with no bounded-degree assumption: if \[ ι_V(G) := \inf_{0<|S|<\infty} \frac{|\partial_V S|}{|S|} >0, \] then $\E_x R_t \ge c(G)(t+1)$ for every starting vertex $x$ and every $t\ge 0$. The proof is direct: vertex expansion implies an unweighted Dirichlet inequality, which in turn gives a uniform positive escape probability from every vertex. We also record a finite counterpart: in an $n$-vertex finite vertex expander, the expected hitting time of an independent stationary random target is $Θ(n)$, again with no restriction on degrees. We also record a chain of geometrically growing lollipops for which \[ \E_o R_t \asymp t^{1/3}, \] so the subdiffusive exponent $1/3$ need not be accompanied by superdiffusive oscillations. In particular, for this graph the lower and upper logarithmic exponents of $\E_oR_t$ are both equal to $1/3$. Finally, since Barnes and Feige proved the sharp universal estimate $\E T_n= O(n^3)$ for the $n$-th discovery time, we move our elementary proof of the weaker bound $\E T_n=O(n^3\log n)$ to a later section as a short self-contained argument with a logarithmic loss. We close with a related bounded-degree mixing statement: if the lazy walk has worst-case mixing time $m$, then at least $c\sqrt m$ starting vertices are still noticeably unmixed at time $\lfloor m/2\rfloor$. This final result uses the same commute-time/effective-resistance control of connected sets that appears throughout the paper.

math.PR↗

Edge-regular graphs with non-negative curvature have polynomial growth

A long-standing conjecture in the emerging discrete Bakry-Émery theory asserts that bounded-degree graphs satisfying $\mathrm{CD}(0,\infty)$ have polynomial growth. In the present paper, we prove this conjecture for all edge-regular graphs, and even obtain a volume doubling estimate with a constant that depends only on the degree. This is made possible thanks to the discovery of a surprising self-improvement phenomenon, which seems of independent interest: any edge-regular graph satisfying $\mathrm{CD}(κ,\infty)$ for some $κ\in\mathbb R$ must in fact satisfy $\mathrm{CD}(κ,n)$ for some explicit, universal and optimal dimension parameter $n$.

math.CO↗

Hyper-contractivity and entropy decay in discrete time

Consider a measure-preserving transition kernel $T$ on an arbitrary probability space $(\mathbb X,\mathcal cA,π)$. In this level of generality, we prove that a one-step hyper-contractivity estimate of the form $\|T\|_{p\to q}\le 1$ with $p< q$ implies a one-step entropy contraction estimate of the form ${\mathrm H}(μT\,|\,π)\le θ\, {\mathrm H}(μ\,|\,π)$, with $θ=p/q$. Neither reversibility, nor any sort of regularity is required. This static implication is simultaneously simpler and stronger than the celebrated dynamic relation between exponential hyper-contractivity and exponential entropy decay along continuous-time Markov semi-groups.

math.PR↗

A transport approach to the cutoff phenomenon

Substantial progress has recently been made in the understanding of the cutoff phenomenon for Markov processes, using an information-theoretic statistics known as varentropy [Sal23; Sal24; Sal25a; PS25]. In the present paper, we propose an alternative approach which bypasses the use of varentropy and exploits instead a new W-TV transport inequality, combined with a classical parabolic regularization estimate [BGL01; OV01]. While currently restricted to non-negatively curved processes on smooth spaces, our argument no longer requires the chain rule, nor any approximate version thereof. As applications, we recover the main result of [Sal25a] establishing cutoff for the log-concave Langevin dynamics, and extend the conclusion to a widely-used discrete-time sampling algorithm known as the Proximal Sampler.

math.PR↗

Modern aspects of Markov chains: entropy, curvature and the cutoff phenomenon

The cutoff phenomenon is an abrupt transition from out of equilibrium to equilibrium undergone by certain Markov processes in the limit where the size of the state space tends to infinity: instead of decaying gradually over time, their distance to equilibrium remains close to its maximal value for a while and suddenly drops to zero as the time parameter reaches a critical threshold. Discovered four decades ago in the context of card shuffling, this surprising phenomenon has since then been observed in a variety of models, from random walks on groups or complex networks to interacting particle systems. It is now believed to be universal among fast-mixing high-dimensional processes. Yet, current proofs are heavily model-dependent, and identifying the general conditions that trigger a cutoff remains one of the biggest challenges in the quantitative analysis of finite Markov chains. The purpose of these lecture notes is to provide a self-contained introduction to this fascinating question, and to describe its recently-uncovered relations with entropy, curvature and concentration.

math.PR↗

Intrinsic regularity in the discrete log-Sobolev inequality

The chain rule lies at the heart of the powerful Gamma calculus for Markov diffusions on manifolds, providing remarkable connections between several fundamental notions such as Bakry-Émery curvature, entropy decay, and hypercontractivity. For Markov chains on finite state spaces, approximate versions of this chain rule have recently been put forward, with an extra cost that depends on the log-Lipschitz regularity of the considered observable. Motivated by those findings, we here investigate the regularity of extremizers in the discrete log-Sobolev inequality. Specifically, we show that their log-Lipschitz constant is bounded by a universal multiple of $\log d$, where $d$ denotes the inverse of the smallest non-zero transition probability. As a consequence, we deduce that the log-Sobolev constant of any reversible Markov chain on a finite state space is at least a universal multiple of $κ/\log d$, where $κ$ is the Bakry-Émery curvature. This is a sharp discrete analogue of what is perhaps the most emblematic application of the Bakry-Émery theory for diffusions. We also obtain a very simple proof of the main result in \cite{MR4620718}, which asserts that the log-Sobolev constant and its modified version agree up to a $\log d$ factor. Our work consolidates the role of the sparsity parameter $\log d$ as a universal cost for transferring results from Markov diffusions to discrete chains.

math.PR↗

A new cutoff criterion for non-negatively curved chains

The cutoff phenomenon was recently shown to systematically follow from non-negative curvature and the product condition, for all Markov diffusions. The proof crucially relied on a classical \emph{chain rule} satisfied by the carré du champ operator, which is specific to differential generators and hence fails on discrete spaces. In the present paper, we show that an approximate version of this chain rule in fact always holds, with an extra cost that depends on the log-Lipschitz regularity of the considered observable. As a consequence, we derive a new cutoff criterion for non-negatively curved chains on finite spaces. The latter allows us to recover, in a simple and unified way, a number of historical instances of cutoff that had been established through model-specific arguments. Emblematic examples include random walk on the hypercube, random transpositions, random walk on the multislice, or MCMC samplers for popular spin systems such as the Ising and Hard-core models on bounded-degree graphs.

math.PR↗

Cutoff for non-negatively curved diffusions

We resolve the long-standing problem of elucidating the cutoff phenomenon for a vast and important class of Markov processes, namely Markov diffusions with non-negative Bakry-Émery curvature. More precisely, we prove that any sequence of non-negatively curved diffusions exhibits cutoff in total variation as soon as the product condition is satisfied. Our result holds in Euclidean spaces as well as on Riemannian manifolds, and for arbitrary non-random initial conditions. It vastly simplifies, unifies and generalizes a number of isolated works that have established cutoff through a delicate and model-dependent analysis of mixing times. The proof is elementary: we exploit a new simple differential relation between varentropy and entropy to produce a quantitative bound on the width of the mixing window.

math.PR↗

Concentration of information on discrete groups

Motivated by the Asymptotic Equipartition Property and its recently discovered role in the cutoff phenomenon, we initiate the systematic study of varentropy on discrete groups. Our main result is an approximate tensorization inequality which asserts that the varentropy of any conjugacy-invariant random walk is, up to a universal multiplicative constant, at most that of the free Abelian random walk with the same jump rates. In particular, it is always bounded by the number d of generators, uniformly in time and in the size of the group. This universal estimate is sharp and can be seen as a discrete analogue of a celebrated result of Bobkov and Madiman concerning random d-dimensional vectors with a log-concave density (AOP 2011). A key ingredient in our proof is the fact that conjugacy-invariant random walks have non-negative Bakry-Émery curvature, a result which seems new and of independent interest.

math.PR↗

Entropy factorization via curvature

We develop a new framework for establishing approximate factorization of entropy on arbitrary probability spaces, using a geometric notion known as non-negative sectional curvature. The resulting estimates are equivalent to entropy subadditivity and generalized Brascamp-Lieb inequalities, and provide a sharp modified log-Sobolev inequality for the Gibbs sampler of several particle systems in both continuous and discrete settings. The method allows us to obtain simple proofs of known results, as well as some new inequalities. We illustrate this through various applications, including discrete Gaussian free fields on arbitrary networks, the down-up walk on uniform $n$-sets, the uniform measure over permutations, and the uniform measure on the unit sphere in $\R^n$. Our method also yields a simple, coupling-based proof of the celebrated logarithmic Sobolev inequality for Langevin diffusions in a convex potential, which is one of the most emblematic applications of the Bakry-Émery criterion.

math.PR↗

Entropy and curvature: beyond the Peres-Tetali conjecture

We study Markov chains with non-negative sectional curvature on finite metric spaces. Neither reversibility, nor the restriction to a particular combinatorial distance are imposed. In this level of generality, we prove that a 1-step contraction in the Wasserstein distance implies a 1-step contraction in relative entropy, by the same amount. Our result substantially strengthens a recent breakthrough of the second author, and has the advantage of being applicable to arbitrary scales. This leads to a time-varying refinement of the standard Modified Log-Sobolev Inequality (MLSI), which allows us to leverage the well-acknowledged fact that curvature improves at large scales. We illustrate this principle with several applications, including birth and death chains, colored exclusion processes, permutation walks, Gibbs samplers for high-temperature spin systems, and attractive zero-range dynamics. In particular, we prove a MLSI with constant equal to the minimal rate increment for the mean-field zero-range process, thereby answering a long-standing question.

math.PR↗

The varentropy criterion is sharp on expanders

The cutoff phenomenon is an abrupt transition from out of equilibrium to equilibrium undergone by certain Markov processes in the limit where the size of the state space tends to infinity: instead of decaying gradually over time, their distance to equilibrium remains close to the maximal value for a while and suddenly drops to zero as the time parameter reaches a critical threshold. Despite the accumulation of many examples, this phenomenon is still far from being understood, and identifying the general conditions that trigger it has become one of the biggest challenges in the quantitative analysis of finite Markov chains. Very recently, the author proposed a general sufficient condition for the occurrence of a cutoff, based on a certain information-theoretical statistics called varentropy. In the present paper, we demonstrate the sharpness of this approach by showing that the cutoff phenomenon is actually equivalent to the varentropy criterion for all sparse, fast-mixing chains. Reversibility is not required.

math.PR↗

Spectral gap and curvature of monotone Markov chains

We prove that the absolute spectral gap of any monotone Markov chain coincides with its optimal Ollivier-Ricci curvature, where the word `optimal' refers to the choice of the underlying metric. Moreover, we provide a new expression in terms of local variations of increasing functions, which has several practical advantages over the traditional variational formulation using the Dirichlet form. As an illustration, we explicitly determine the optimal curvature and spectral gap of the non-conservative exclusion process with heterogeneous reservoir densities on any network, despite the lack of reversibility.

math.PR↗

Upgrading MLSI to LSI for reversible Markov chains

For reversible Markov chains on finite state spaces, we show that the modified log-Sobolev inequality (MLSI) can be upgraded to a log-Sobolev inequality (LSI) at the surprisingly low cost of degrading the associated constant by $\log (1/p)$, where $p$ is the minimum non-zero transition probability. We illustrate this by providing the first log-Sobolev estimate for Zero-Range processes on arbitrary graphs. As another application, we determine the modified log-Sobolev constant of the Lamplighter chain on all bounded-degree graphs, and use it to provide negative answers to two open questions by Montenegro and Tetali (2006) and Hermon and Peres (2018). Our proof builds upon the `regularization trick' recently introduced by the last two authors.

math.PR↗