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Robert J. McCann

Publications and source records attributed to Robert J. McCann.

At least 37 records · Page 2Linked to original sources

On the cardinality of sets in ${\bf R}^d$ obeying a slightly obtuse angle bound

In this paper we explicitly estimate the number of points in a subset $A \subset \R^{d}$ as a function of the maximum angle $\angle A$ that any three of these points form, provided $\angle A < θ_d := \arccos(-\frac 1 {d}) \in (π/2,π)$. We also show $\angle A < θ_d$ ensures that $A$ coincides with the vertex set of a convex polytope. This study is motivated by a question of Paul Erdős and indirectly by a conjecture of László Fejes Tóth.

math.MG↗

Maximizing expected powers of the angle between pairs of points in projective space

Among probability measures on $d$-dimensional real projective space, one which maximizes the expected angle $\arccos(\frac{x}{|x|}\cdot \frac{y}{|y|})$ between independently drawn projective points $x$ and $y$ was conjectured to equidistribute its mass over the standard Euclidean basis $\{e_0,e_1,\ldots, e_d\}$ by Fejes Tóth \cite{FT59}. If true, this conjecture evidently implies the same measure maximizes the expectation of $\arccos^α(\frac{x}{|x|}\cdot \frac{y}{|y|})$ for any exponent $α> 1$. The kernel $\arccos^α(\frac{x}{|x|}\cdot \frac{y}{|y|})$ represents the objective of an infinite-dimensional quadratic program. We verify discrete and continuous versions of this {milder} conjecture in a non-empty range $α> α_{Δ^d} \ge 1$, and establish uniqueness of the resulting maximizer $\hat μ$ up to rotation. We show $\hat μ$ no longer maximizes when $α<α_{Δ^d}$. At the endpoint $α=α_{Δ^d}$ of this range, we show another maximizer $μ$ must also exist which is not a rotation of $\hat μ$. For the continuous version of the conjecture, an appendix provided by Bilyk et al in response to an earlier draft of this work combines with the present improvements to yield $α_{Δ^d}<2$. The original conjecture $\ald=1$ remains open (unless $d=1$). However, in the maximum possible range $α>1$, we show $\hat μ$ and its rotations maximize the aforementioned expectation uniquely on a sufficiently small ball in the $L^\infty$-Kantorovich-Rubinstein-Wasserstein metric $d_\infty$ from optimal transportation; the same is true for any measure $μ$ which is mutually absolutely continuous with respect to $\hat μ$, but the size of the ball depends on {$α,d$, and} $\|\frac{d \hat μ}{dμ}\|_{\infty}$.

math.MG↗

A Lorentzian analog for Hausdorff dimension and measure

We define a one-parameter family of canonical volume measures on Lorentzian (pre-)length spaces. In the Lorentzian setting, this allows us to define a geometric dimension - akin to the Hausdorff dimension for metric spaces - that distinguishes between e.g. spacelike and null subspaces of Minkowski spacetime. The volume measure corresponding to its geometric dimension gives a natural reference measure on a synthetic or limiting spacetime, and allows us to define what it means for such a spacetime to be collapsed (in analogy with metric measure geometry and the theory of Riemannian Ricci limit spaces). As a crucial tool we introduce a doubling condition for causal diamonds and a notion of causal doubling measures. Moreover, applications to continuous spacetimes and connections to synthetic timelike curvature bounds are given.

math-ph↗

On Fejes Tóth's conjectured maximizer for the sum of angles between lines

Choose $N$ unoriented lines through the origin of ${\bf R}^{d+1}$. The sum of the angles between these lines is conjectured to be maximized if the lines are distributed as evenly as possible amongst the coordinate axes of some orthonormal basis for ${\bf R}^{d+1}$. For $d \ge 2$ we embed the conjecture into a one-parameter family of problems, in which we seek to maximize the sum of the $α$-th power of the renormalized angles between the lines. We show the conjecture is equivalent to this same configuration becoming the {\em unique} optimizer (up to rotations) for all $α>1$. We establish both the asserted optimality and uniqueness in the limiting case $α=\infty$ of mildest repulsion. The same conclusions extend to $N=\infty$, provided we assume only finitely many of the lines are distinct.

math.MG↗

Inscribed Radius Bounds for Lower Ricci Bounded Metric Measure Spaces with Mean Convex Boundary

Consider an essentially nonbranching metric measure space with the measure contraction property of Ohta and Sturm, or with a Ricci curvature lower bound in the sense of Lott, Sturm and Villani. We prove a sharp upper bound on the inscribed radius of any subset whose boundary has a suitably signed lower bound on its generalized mean curvature. This provides a nonsmooth analog to a result of Kasue (1983) and Li (2014). We prove a stability statement concerning such bounds and - in the Riemannian curvature-dimension (RCD) setting - characterize the cases of equality.

math.DG↗

Geometrical bounds for the variance and recentered moments

We bound the variance and other moments of a random vector based on the range of its realizations, thus generalizing inequalities of Popoviciu (1935) and Bhatia and Davis (2000) concerning measures on the line to several dimensions. This is done using convex duality and (infinite-dimensional) linear programming. The following consequence of our bounds exhibits symmetry breaking, provides a new proof of Jung's theorem (1901), and turns out to have applications to the aggregation dynamics modelling attractive-repulsive interactions: among probability measures on ${\mathbf R}^n$ whose support has diameter at most $\sqrt{2}$, we show that the variance around the mean is maximized precisely by those measures which assign mass $1/(n+1)$ to each vertex of a standard simplex. For $1 \le p <\infty$, the $p$-th moment --- optimally centered --- is maximized by the same measures among those satisfying the diameter constraint.

math.PR↗

Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds

In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a quantitative topology point of view. In particular, we show that no DR maps can achieve perfect precision and perfect recall simultaneously. Thus a continuous DR map must have imperfect precision. We further prove an upper bound on the precision of Lipschitz continuous DR maps. While precision is a natural measure in an information retrieval setting, it does not measure `how' wrong the retrieved data is. We therefore propose a new measure based on Wasserstein distance that comes with similar theoretical guarantee. A key technical step in our proofs is a particular optimization problem of the $L_2$-Wasserstein distance over a constrained set of distributions. We provide a complete solution to this optimization problem, which can be of independent interest on the technical side.

stat.ML↗

On concavity of the monopolist's problem facing consumers with nonlinear price preferences

A monopolist wishes to maximize her profits by finding an optimal price policy. After she announces a menu of products and prices, each agent $x$ will choose to buy that product $y(x)$ which maximizes his own utility, if positive. The principal's profits are the sum of the net earnings produced by each product sold. These are determined by the costs of production and the distribution of products sold, which in turn are based on the distribution of anonymous agents and the choices they make in response to the principal's price menu. In this paper, we provide a necessary and sufficient condition for the convexity or concavity of the principal's (bilevel) optimization problem, assuming each agent's disutility is a strictly increasing but not necessarily affine (i.e. quasilinear) function of the price paid. Concavity when present, makes the problem more amenable to computational and theoretical analysis; it is key to obtaining uniqueness and stability results for the principal's strategy in particular. Even in the quasilinear case, our analysis goes beyond previous work by addressing convexity as well as concavity, by establishing conditions which are not only sufficient but necessary, and by requiring fewer hypotheses on the agents' preferences.

math.OC↗

Academic wages and pyramid schemes: a mathematical model

This paper analyzes a steady state matching model interrelating the education and labor sectors. In this model, a heterogeneous population of students match with teachers to enhance their cognitive skills. As adults, they then choose to become workers, managers, or teachers, who match in the labor or educational market to earn wages by producing output. We study the competitive equilibrium which results from the steady state requirement that the educational process replicate the same endogenous distribution of cognitive skills among adults in each generation (assuming the same distribution of student skills). We show such an equilibrium can be found by solving an infinite-dimensional linear program and its dual. We analyze the structure of our solutions, and give sufficient conditions for them to be unique. Whether or not the educational matching is positive assortative turns out to depend on convexity of the equilibrium wages as a function of ability, suitably parameterized; we identity conditions which imply this convexity. Moreover, due to the recursive nature of the education market, it is a priori conceivable that a pyramid scheme leads to greater and greater discrepancies in the wages of the most talented teachers at the top of the market. Assuming each teacher teaches $N$ students, and contributes a fraction $θ\in]0,1[$ to their cognitive skill, we show a phase transition occurs at $Nθ=1$, which determines whether or not the wage gradients of these teachers remain bounded as market size grows, and make a quantitative prediction for their asymptotic behaviour in both regimes: $Nθ\ge 1$ and $Nθ<1$.

math.OC↗

The intrinsic dynamics of optimal transport

The question of which costs admit unique optimizers in the Monge-Kantorovich problem of optimal transportation between arbitrary probability densities is investigated. For smooth costs and densities on compact manifolds, the only known examples for which the optimal solution is always unique require at least one of the two underlying spaces to be homeomorphic to a sphere. We introduce a (multivalued) dynamics which the transportation cost induces between the target and source space, for which the presence or absence of a sufficiently large set of periodic trajectories plays a role in determining whether or not optimal transport is necessarily unique. This insight allows us to construct smooth costs on a pair of compact manifolds with arbitrary topology, so that the optimal transportation between any pair of probility densities is unique.

math.OC↗

Dual potentials for capacity constrained optimal transport

Optimal transportation with capacity constraints, a variant of the well-known optimal transportation problem, is concerned with transporting one probability density $f \in L^1(\mathbb{R}^m)$ onto another one $g \in L^1(\mathbb{R}^n)$ so as to optimize a cost function $c \in L^1(\mathbb{R}^{m+n})$ while respecting the capacity constraints $0\le h \le \bar h\in L^\infty(\mathbb{R}^{m+n})$. A linear programming duality theorem for this problem was first established by Levin. In this note, we prove under mild assumptions on the given data, the existence of a pair of $L^1$-functions optimizing the dual problem. Using these functions, which can be viewed as Lagrange multipliers to the marginal constraints $f$ and $g$, we characterize the solution $h$ of the primal problem. We expect these potentials to play a key role in any further analysis of $h$. Moreover, starting from Levin's duality, we derive the classical Kantorovich duality for unconstrained optimal transport. In tandem with results obtained in our companion paper (arXiv:1309.3022), this amounts to a new and elementary proof of Kantorovich's duality.

math.OC↗

The spectrum of a family of fourth-order equations near the global attractor

The thin-film and quantum drift diffusion equations belong to a fourth-order family of evolution equations proposed in ref. 16 to be analogous to the (second-order) porous medium family. They are $2$-Wasserstein gradient ($W_2$) flows of the generalized Fisher information $I(u)$ just as the porous medium family was shown to be the $W_2$ gradient flow of the generalized entropy $E(u)$ by Otto. The identity $I(u) = |\nabla_{W_2} E(u)|^2/2$ (formally) becomes $\Hess_{W_2} I(u_*) =\Hess_{W_2}^2 E(u_*)$ when linearizing the equation around its self-similar solution $u_*$. We couple this relation with the diagonalization of $\Hess_{W_2} E(u_*)$ for the porous medium flow computed in ref. 38. This yields information about the leading- and higher-order asymptotics of the equation on $\R^N$ which --- outside of special cases --- was inaccessible previously.

math.AP↗

Insights into capacity constrained optimal transport

A variant of the classical optimal transportation problem is: among all joint measures with fixed marginals and which are dominated by a given density, find the optimal one. Existence and uniqueness of solutions to this variant were established in \cite{KM11}. In the present manuscript, we expose an unexpected symmetry leading to the first explicit examples in two and more dimensions. These are inspired in part by simulations in one dimension which display singularities and topology and in part by two further developments: the identification of all extreme points in the feasible set, and a new approach to uniqueness based on constructing feasible perturbations.

math.OC↗

Optimal Transportation with Capacity Constraints

The classical problem of optimal transportation can be formulated as a linear optimization problem on a convex domain: among all joint measures with fixed marginals find the optimal one, where optimality is measured against a cost function. Here we consider a natural but largely unexplored variant of this problem by imposing a pointwise constraint on the joint (absolutely continuous) measures: among all joint densities with fixed marginals and which are dominated by a given density, find the optimal one. For this variant, we show local non-degeneracy of the cost function implies every minimizer is extremal in the convex set of competitors, hence unique. An appendix develops rudiments of a duality theory for this problem, which allows us to compute several suggestive examples.

math.OC↗

Unique equilibria and substitution effects in a stochastic model of the marriage market

Choo-Siow (2006) proposed a model for the marriage market which allows for random identically distributed noise in the preferences of each of the participants. The randomness is McFadden-type, which permits an explicit resolution of the equilibrium preference probabilities. The purpose of this note is to prove uniqueness of the resulting equilibrium marriage distribution, and find a representation of it in closed form. This allows us to derive smooth dependence of this distribution on exogenous preference and population parameters, and establish sign, symmetry, and size of the various substitution effects, facilitating comparative statics. For example, we show that an increase in the population of men of any given type in this model leads to an increase in single men of each type, and a decrease in single women of each type. We show that an increase in the number of men of a given type increases the equilibrium transfer paid by such men to their spouses, and also increases the percentage of men of that type who choose to remain unmarried. The verification of such properties helps to substantiate the validity of the model. Moreover, we make unexpected predictions which could be tested: the percentage change of type $i$ unmarrieds with respect to fluctuations in the total number of type $j$ men or women turns out to form a symmetric positive-definite matrix $r_{ij}=r_{ji}$ in this model, and thus to satisfy bounds like $|r_{ij}| \le (r_{ii}r_{jj})^{1/2}$. Along the way, we give a new proof for the existence of an equilibrium, based on a strictly convex variational principle and a simple estimate, rather than a fixed point theorem. Fixed point approaches to the existence part of our result have been explored by others \cite{CSS} \cite{Dag} \cite{Fox}, but are much more complicated and yield neither uniqueness, nor comparative statics, nor an explicit representation of the solution.

math.OC↗

Hölder continuity and injectivity of optimal maps

Consider transportation of one distribution of mass onto another, chosen to optimize the total expected cost, where cost per unit mass transported from x to y is given by a smooth function c(x,y). If the source density f^+(x) is bounded away from zero and infinity in an open region U' \subset R^n, and the target density f^-(y) is bounded away from zero and infinity on its support \cl V \subset R^n, which is strongly c-convex with respect to U', and the transportation cost c satisfies the A3w condition of Trudinger and Wang \cite{TrudingerWang07p}, we deduce local Hölder continuity and injectivity of the optimal map inside U' (so that the associated potential u belongs to C^{1,α}_{loc}(\U')). Here the exponent α>0 depends only on the dimension and the bounds on the densities, but not on c. Our result provides a crucial step in the low/interior regularity setting: in a sequel \cite{FigalliKimMcCann09p}, we use it to establish regularity of optimal maps with respect to the Riemannian distance squared on arbitrary products of spheres. Three key tools are introduced in the present paper. Namely, we first find a transformation that under A3w makes c-convex functions level-set convex (as was also obtained independently from us by Liu \cite{Liu09}). We then derive new Alexandrov type estimates for the level-set convex c-convex functions, and a topological lemma showing optimal maps do not mix interior with boundary. This topological lemma, which does not require A3w, is needed by Figalli and Loeper \cite{FigalliLoeper08p} to conclude continuity of optimal maps in two dimensions. In higher dimensions, if the densities f^\pm are Hölder continuous, our result permits continuous differentiability of the map inside U' (in fact, C^{2,α}_{loc} regularity of the associated potential) to be deduced from the work of Liu, Trudinger and Wang \cite{LiuTrudingerWang09p}.

math.AP↗

On supporting hyperplanes to convex bodies

Given a convex set and an interior point close to the boundary, we prove the existence of a supporting hyperplane whose distance to the point is controlled, in a dimensionally quantified way, by the thickness of the convex set in the orthogonal direction. This result has important applications in the regularity theory for Monge-Ampère type equations arising in optimal transportation.

math.AP↗