arXiv subjects

James R. Lee

Publications and source records attributed to James R. Lee.

At least 37 records · Page 2Linked to original sources

arXiv2017

k-server via multiscale entropic regularization

We present an $O((\log k)^2)$-competitive randomized algorithm for the $k$-server problem on hierarchically separated trees (HSTs). This is the first $o(k)$-competitive randomized algorithm for which the competitive ratio is independent of the size of the underlying HST. Our algorithm is designed in the framework of online mirror descent where the mirror map is a multiscale entropy. When combined with Bartal's static HST embedding reduction, this leads to an $O((\log k)^2 \log n)$-competitive algorithm on any $n$-point metric space. We give a new dynamic HST embedding that yields an $O((\log k)^3 \log Δ)$-competitive algorithm on any metric space where the ratio of the largest to smallest non-zero distance is at most $Δ$.

cs.DS↗

arXiv2017

Regularization under diffusion and anti-concentration of the information content

Under the Ornstein-Uhlenbeck semigroup $\{U_t\}$, any non-negative measurable $f : \mathbb R^n \to \mathbb R_+$ exhibits a uniform tail bound better than that implied by Markov's inequality and conservation of mass: For every $α\geq e^3$, and $t > 0$, \[ γ_n\left(\left\{x \in \mathbb R^n : U_t f(x) > α\int f\,dγ_n\right\}\right) \leq C(t) \frac{1}α \sqrt{\frac{\log \log α}{\log α}}\] where $γ_n$ is the $n$-dimensional Gaussian measure and $C(t)$ is a constant depending only on $t$. This confirms positively the Gaussian limiting case of Talagrand's convolution conjecture (1989). This is shown to follow from a more general phenomenon. Suppose that $f : \mathbb{R}^n \to \mathbb{R}_+$ is {\em semi-log-convex} in the sense that for some $β> 0$, for all $x \in \mathbb{R}^n$, the eigenvalues of $\nabla^2 \log f(x)$ are at least $-β$. Then $f$ satisfies a tail bound asymptotically better than that implied by Markov's inequality.

math.PR↗

arXiv2017

Separators in region intersection graphs

For undirected graphs $G=(V,E)$ and $G_0=(V_0,E_0)$, say that $G$ is a region intersection graph over $G_0$ if there is a family of connected subsets $\{ R_u \subseteq V_0 : u \in V \}$ of $G_0$ such that $\{u,v\} \in E \iff R_u \cap R_v \neq \emptyset$. We show if $G_0$ excludes the complete graph $K_h$ as a minor for some $h \geq 1$, then every region intersection graph $G$ over $G_0$ with $m$ edges has a balanced separator with at most $c_h \sqrt{m}$ nodes, where $c_h$ is a constant depending only on $h$. If $G$ additionally has uniformly bounded vertex degrees, then such a separator is found by spectral partitioning. A string graph is the intersection graph of continuous arcs in the plane. The preceding result implies that every string graph with $m$ edges has a balanced separator of size $O(\sqrt{m})$. This bound is optimal, as it generalizes the planar separator theorem. It confirms a conjecture of Fox and Pach (2010), and improves over the $O(\sqrt{m} \log m)$ bound of Matousek (2013).

math.CO↗

arXiv2016

Covering the large spectrum and generalized Riesz products

Chang's Lemma is a widely employed result in additive combinatorics. It gives bounds on the dimension of the large spectrum of probability distributions on finite abelian groups. Recently, Bloom (2016) presented a powerful variant of Chang's Lemma that yields the strongest known quantitative version of Roth's theorem on 3-term arithmetic progressions in dense subsets of the integers. In this note, we show how such theorems can be derived from the approximation of probability measures via entropy maximization.

math.CO↗

arXiv2016

Transport-entropy inequalities and curvature in discrete-space Markov chains

We show that if the random walk on a graph has positive coarse Ricci curvature in the sense of Ollivier, then the stationary measure satisfies a W^1 transport-entropy inequality. Peres and Tetali have conjectured a stronger consequence, that a modified log-Sobolev inequality (MLSI) should hold, in analogy with the setting of Markov diffusions. We discuss how our entropy interpolation approach suggests a natural attack on the MLSI conjecture.

math.PR↗

arXiv2016

Diffusive estimates for random walks on stationary random graphs of polynomial growth

Let $(G,ρ)$ be a stationary random graph, and use $B^G_ρ(r)$ to denote the ball of radius $r$ about $ρ$ in $G$. Suppose that $(G,ρ)$ has annealed polynomial growth, in the sense that $\mathbb{E}[|B^G_ρ(r)|] \leq O(r^k)$ for some $k > 0$ and every $r \geq 1$. Then there is an infinite sequence of times $\{t_n\}$ at which the random walk $\{X_t\}$ on $(G,ρ)$ is at most diffusive: Almost surely (over the choice of $(G,ρ)$), there is a number $C > 0$ such that \[ \mathbb{E} \left[\mathrm{dist}_G(X_0, X_{t_n})^2 \mid X_0 = ρ, (G,ρ)\right]\leq C t_n\qquad \forall n \geq 1\,. \] This result is new even in the case when $G$ is a stationary random subgraph of $\mathbb{Z}^d$. Combined with the work of Benjamini, Duminil-Copin, Kozma, and Yadin (2015), it implies that $G$ almost surely does not admit a non-constant harmonic function of sublinear growth. To complement this, we argue that passing to a subsequence of times $\{t_n\}$ is necessary, as there are stationary random graphs of (almost sure) polynomial growth where the random walk is almost surely superdiffusive at an infinite subset of times.

math.PR↗

arXiv2016

Approximate Constraint Satisfaction Requires Large LP Relaxations

We prove super-polynomial lower bounds on the size of linear programming relaxations for approximation versions of constraint satisfaction problems. We show that for these problems, polynomial-sized linear programs are exactly as powerful as programs arising from a constant number of rounds of the Sherali-Adams hierarchy. In particular, any polynomial-sized linear program for Max Cut has an integrality gap of 1/2 and any such linear program for Max 3-Sat has an integrality gap of 7/8.

cs.CC↗

arXiv2015

A Gaussian upper bound for martingale small-ball probabilities

Consider a discrete-time martingale $\{X_t\}$ taking values in a Hilbert space $\mathcal H$. We show that if for some $L \geq 1$, the bounds $\mathbb{E} \left[\|X_{t+1}-X_t\|_{\mathcal H}^2 \mid X_t\right]=1$ and $\|X_{t+1}-X_t\|_{\mathcal H} \leq L$ are satisfied for all times $t \geq 0$, then there is a constant $c = c(L)$ such that for $1 \leq R \leq \sqrt{t}$, \[\mathbb{P}(\|X_t\|_{\mathcal H} \leq R \mid X_0 = x_0) \leq c \frac{R}{\sqrt{t}} e^{-\|x_0\|_{\mathcal H}^2/(6 L^2 t)}\,.\] Following [Lee-Peres, Ann. Probab. 2013], this has applications to diffusive estimates for random walks on vertex-transitive graphs.

math.PR↗

arXiv2014

Lower bounds on the size of semidefinite programming relaxations

We introduce a method for proving lower bounds on the efficacy of semidefinite programming (SDP) relaxations for combinatorial problems. In particular, we show that the cut, TSP, and stable set polytopes on $n$-vertex graphs are not the linear image of the feasible region of any SDP (i.e., any spectrahedron) of dimension less than $2^{n^c}$, for some constant $c > 0$. This result yields the first super-polynomial lower bounds on the semidefinite extension complexity of any explicit family of polytopes. Our results follow from a general technique for proving lower bounds on the positive semidefinite rank of a matrix. To this end, we establish a close connection between arbitrary SDPs and those arising from the sum-of-squares SDP hierarchy. For approximating maximum constraint satisfaction problems, we prove that SDPs of polynomial-size are equivalent in power to those arising from degree-$O(1)$ sum-of-squares relaxations. This result implies, for instance, that no family of polynomial-size SDP relaxations can achieve better than a 7/8-approximation for MAX-3-SAT.

cs.CC↗

arXiv2014

Multi-way spectral partitioning and higher-order Cheeger inequalities

A basic fact in spectral graph theory is that the number of connected components in an undirected graph is equal to the multiplicity of the eigenvalue zero in the Laplacian matrix of the graph. In particular, the graph is disconnected if and only if there are at least two eigenvalues equal to zero. Cheeger's inequality and its variants provide an approximate version of the latter fact; they state that a graph has a sparse cut if and only if there are at least two eigenvalues that are close to zero. It has been conjectured that an analogous characterization holds for higher multiplicities, i.e., there are $k$ eigenvalues close to zero if and only if the vertex set can be partitioned into $k$ subsets, each defining a sparse cut. We resolve this conjecture. Our result provides a theoretical justification for clustering algorithms that use the bottom $k$ eigenvectors to embed the vertices into $\mathbb R^k$, and then apply geometric considerations to the embedding. We also show that these techniques yield a nearly optimal tradeoff between the expansion of sets of size $\approx n/k$, and the $k$th smallest eigenvalue of the normalized Laplacian matrix, denoted $λ_k$. In particular, we show that in every graph there is a set of size at most $2n/k$ which has expansion at most $O(\sqrt{λ_k \log k})$. This bound is tight, up to constant factors, for the "noisy hypercube" graphs.

math.MG↗

arXiv2013

Harmonic maps on amenable groups and a diffusive lower bound for random walks

We prove diffusive lower bounds on the rate of escape of the random walk on infinite transitive graphs. Similar estimates hold for finite graphs, up to the relaxation time of the walk. Our approach uses nonconstant equivariant harmonic mappings taking values in a Hilbert space. For the special case of discrete, amenable groups, we present a more explicit proof of the Mok-Korevaar-Schoen theorem on the existence of such harmonic maps by constructing them from the heat flow on a Følner set.

math.PR↗

arXiv2013

Markov type and threshold embeddings

For two metric spaces X and Y, say that X {threshold-embeds} into Y if there exist a number K > 0 and a family of Lipschitz maps $f_τ : X \to Y : τ> 0 \}$ such that for every $x,y \in X$, \[ d_X(x,y) \geq τ=> d_Y(f_τ(x),f_τ(y)) \geq \|φ_τ\|_{\Lip} τ/K \] where $\|f_τ\|_{\Lip}$ denotes the Lipschitz constant of $f_τ$. We show that if a metric space X threshold-embeds into a Hilbert space, then X has Markov type 2. As a consequence, planar graph metrics and doubling metrics have Markov type 2, answering questions of Naor, Peres, Schramm, and Sheffield. More generally, if a metric space X threshold-embeds into a p-uniformly smooth Banach space, then X has Markov type p. This suggests some non-linear analogs of Kwapien's theorem. For instance, a subset $X \subseteq L_1$ threshold-embeds into Hilbert space if and only if X has Markov type 2.

math.MG↗

arXiv2013

A lower bound on dimension reduction for trees in \ell_1

There is a constant c > 0 such that for every $ε\in (0,1)$ and $n \geq 1/ε^2$, the following holds. Any mapping from the $n$-point star metric into $\ell_1^d$ with bi-Lipschitz distortion $1+ε$ requires dimension $$d \geq {c\log n\over ε^2\log (1/ε)}.$$

math.MG↗

arXiv2012

Pathwidth, trees, and random embeddings

We prove that, for every $k=1,2,...,$ every shortest-path metric on a graph of pathwidth $k$ embeds into a distribution over random trees with distortion at most $c$ for some $c=c(k)$. A well-known conjecture of Gupta, Newman, Rabinovich, and Sinclair states that for every minor-closed family of graphs $F$, there is a constant $c(F)$ such that the multi-commodity max-flow/min-cut gap for every flow instance on a graph from $F$ is at most $c(F)$. The preceding embedding theorem is used to prove this conjecture whenever the family $F$ does not contain all trees.

math.MG↗

arXiv2012

A node-capacitated Okamura-Seymour theorem

The classical Okamura-Seymour theorem states that for an edge-capacitated, multi-commodity flow instance in which all terminals lie on a single face of a planar graph, there exists a feasible concurrent flow if and only if the cut conditions are satisfied. Simple examples show that a similar theorem is impossible in the node-capacitated setting. Nevertheless, we prove that an approximate flow/cut theorem does hold: For some universal c > 0, if the node cut conditions are satisfied, then one can simultaneously route a c-fraction of all the demands. This answers an open question of Chekuri and Kawarabayashi. More generally, we show that this holds in the setting of multi-commodity polymatroid networks introduced by Chekuri, et. al. Our approach employs a new type of random metric embedding in order to round the convex programs corresponding to these more general flow problems.

math.CO↗

arXiv2012

On the Hausdorff dimension of ultrametric subsets in R^n

For every e>0, any subset of R^n with Hausdorff dimension larger than (1-e)n must have ultrametric distortion larger than 1/(4e).

math.MG↗

arXiv2012

A note on mixing times of planar random walks

We present an infinite family of finite planar graphs $\{X_n\}$ with degree at most five and such that for some constant $c > 0$, $$ λ_1(X_n) \geq c(\frac{\log \diam(X_n)}{\diam(X_n)})^2\,, $$ where $λ_1$ denotes the smallest non-zero eigenvalue of the graph Laplacian. This significantly simplifies a construction of Louder and Souto. We also remark that such a lower bound cannot hold when the diameter is replaced by the average squared distance: There exists a constant $c > 0$ such that for any family $\{X_n\}$ of planar graphs we have $$ λ_1(X_n) \leq c (\frac{1}{|X_n|^2} \sum_{x,y \in X_n} d(x,y)^2)^{-1}\,, $$ where $d$ denotes the path metric on $X_n$.

math.PR↗

arXiv2011

Cover times, blanket times, and majorizing measures

We exhibit a strong connection between cover times of graphs, Gaussian processes, and Talagrand's theory of majorizing measures. In particular, we show that the cover time of any graph $G$ is equivalent, up to universal constants, to the square of the expected maximum of the Gaussian free field on $G$, scaled by the number of edges in $G$. This allows us to resolve a number of open questions. We give a deterministic polynomial-time algorithm that computes the cover time to within an O(1) factor for any graph, answering a question of Aldous and Fill (1994). We also positively resolve the blanket time conjectures of Winkler and Zuckerman (1996), showing that for any graph, the blanket and cover times are within an O(1) factor. The best previous approximation factor for both these problems was $O((\log \log n)^2)$ for $n$-vertex graphs, due to Kahn, Kim, Lovasz, and Vu (2000).

math.PR↗