Results 1  10
of
48
The geometry of optimal transportation
 Acta Math
, 1996
"... A classical problem of transporting mass due to Monge and Kantorovich is solved. Given measures µ and ν on R d, we find the measurepreserving map y(x) between them with minimal cost — where cost is measured against h(x − y) withhstrictly convex, or a strictly concave function of x − y. This map i ..."
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Cited by 144 (31 self)
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A classical problem of transporting mass due to Monge and Kantorovich is solved. Given measures µ and ν on R d, we find the measurepreserving map y(x) between them with minimal cost — where cost is measured against h(x − y) withhstrictly convex, or a strictly concave function of x − y. This map is unique: it is characterized by the formula y(x) =x−(∇h) −1 (∇ψ(x)) and geometrical restrictions on ψ. Connections with mathematical economics, numerical computations, and the MongeAmpère equation are sketched. ∗ Both authors gratefully acknowledge the support provided by postdoctoral fellowships: WG at
Shape Matching: Similarity Measures and Algorithms
, 2001
"... Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties o ..."
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Cited by 91 (1 self)
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Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties of the similarity measure that are needed for the problem, choosing the specific similarity measure, and constructing the algorithm to compute the similarity. The focus is on methods that lie close to the field of computational geometry.
Differential equations methods for the MongeKantorovich mass transfer problem
 Mem. Amer. Math. Soc
, 1999
"... We demonstrate that a solution to the classical Monge–Kantorovich problem of optimally rearranging the measure µ + = f + dx onto µ − = f − dy can be constructed by studying the pLaplacian equation −div(Dup  p−2 Dup) = f + − f − in the limit as p → ∞. The idea is to show up → u, where u satisfie ..."
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Cited by 87 (8 self)
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We demonstrate that a solution to the classical Monge–Kantorovich problem of optimally rearranging the measure µ + = f + dx onto µ − = f − dy can be constructed by studying the pLaplacian equation −div(Dup  p−2 Dup) = f + − f − in the limit as p → ∞. The idea is to show up → u, where u satisfies Du  ≤ 1, −div(aDu) = f + − f − for some density a ≥ 0, and then to build a flow by solving an ODE involving a, Du, f + and f −. Contents 1.
Exponential integrability and transportation cost related to logarithmic Sobolev inequalities
 J. Funct. Anal
, 1999
"... We study some problems on exponential integrability, concentration of measure, and transportation cost related to logarithmic Sobolev inequalities. On the real line, we then give a characterization of those probability measures which satisfy these inequalities 1999 Academic Press Key Words: logarith ..."
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Cited by 80 (4 self)
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We study some problems on exponential integrability, concentration of measure, and transportation cost related to logarithmic Sobolev inequalities. On the real line, we then give a characterization of those probability measures which satisfy these inequalities 1999 Academic Press Key Words: logarithmic Sobolev inequalities; exponential integrability; concentration of measure; transportation inequalities.
Perspectives of Monge Properties in Optimization
, 1995
"... An m × n matrix C is called Monge matrix if c ij + c rs c is + c rj for all 1 i ! r m, 1 j ! s n. In this paper we present a survey on Monge matrices and related Monge properties and their role in combinatorial optimization. Specifically, we deal with the following three main topics: (i) funda ..."
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Cited by 54 (3 self)
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An m × n matrix C is called Monge matrix if c ij + c rs c is + c rj for all 1 i ! r m, 1 j ! s n. In this paper we present a survey on Monge matrices and related Monge properties and their role in combinatorial optimization. Specifically, we deal with the following three main topics: (i) fundamental combinatorial properties of Monge structures, (ii) applications of Monge properties to optimization problems and (iii) recognition of Monge properties.
Probability Metrics and Recursive Algorithms
"... In this paper it is shown by several examples that probability metrics are a useful tool to study the asymptotic behaviour of (stochastic) recursive algorithms. The basic idea of this approach is to find a `suitable ' probability metric which yields contraction properties of the transformations des ..."
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Cited by 47 (9 self)
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In this paper it is shown by several examples that probability metrics are a useful tool to study the asymptotic behaviour of (stochastic) recursive algorithms. The basic idea of this approach is to find a `suitable ' probability metric which yields contraction properties of the transformations describing the limits of the algorithm. In order to demonstrate the wide range of applicability of this contraction method we investigate examples from various fields, some of them have been analyzed already in the literature.
An efficient earth mover’s distance algorithm for robust histogram comparison
 PAMI
, 2007
"... DRAFT We propose EMDL1: a fast and exact algorithm for computing the Earth Mover’s Distance (EMD) between a pair of histograms. The efficiency of the new algorithm enables its application to problems that were previously prohibitive due to high time complexities. The proposed EMDL1 significantly s ..."
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Cited by 44 (4 self)
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DRAFT We propose EMDL1: a fast and exact algorithm for computing the Earth Mover’s Distance (EMD) between a pair of histograms. The efficiency of the new algorithm enables its application to problems that were previously prohibitive due to high time complexities. The proposed EMDL1 significantly simplifies the original linear programming formulation of EMD. Exploiting the L1 metric structure, the number of unknown variables in EMDL1 is reduced to O(N) from O(N 2) of the original EMD for a histogram with N bins. In addition, the number of constraints is reduced by half and the objective function of the linear program is simplified. Formally without any approximation, we prove that the EMDL1 formulation is equivalent to the original EMD with a L1 ground distance. To perform the EMDL1 computation, we propose an efficient treebased algorithm, TreeEMD. TreeEMD exploits the fact that a basic feasible solution of the simplex algorithmbased solver forms a spanning tree when we interpret EMDL1 as a network flow optimization problem. We empirically show that this new algorithm has average time complexity of O(N 2), which significantly improves the best reported supercubic complexity of the original EMD. The accuracy of the proposed methods is evaluated by
Stability analysis for stochastic programs
 ANNALS OF OPERATIONS RESEARCH
, 1991
"... For stochastic programs with recourse and with (several joint) probabilistic constraints, respectively, we derive quantitative continuity properties of the relevant expectation functionals and constraint set mappings. This leads to qualitative and quantitative stability results for optimal values an ..."
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Cited by 25 (15 self)
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For stochastic programs with recourse and with (several joint) probabilistic constraints, respectively, we derive quantitative continuity properties of the relevant expectation functionals and constraint set mappings. This leads to qualitative and quantitative stability results for optimal values and optimal solutions with respect to perturbations of the underlying probability distributions. Earlier stability results for stochastic programs with recourse and for those with probabilistic constraints are refined and extended, respectively. Emphasis is placed on equipping sets of probability measures with metrics that one can handle in specific situations. To illustrate the general stability results we present possible consequences when estimating the original probability measure via empirical ones.
Shape recognition via Wasserstein distance
 Appl. Math
, 1999
"... The KantorovichRubinsteinWasserstein metric defines the distance between two probability measures µ and ν on R d+1 by computing the cheapest way to transport the mass of µ onto ν, where the cost per unit mass transported is a given function c(x, y)onR 2d+2. Motivated by applications to shape recog ..."
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Cited by 23 (14 self)
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The KantorovichRubinsteinWasserstein metric defines the distance between two probability measures µ and ν on R d+1 by computing the cheapest way to transport the mass of µ onto ν, where the cost per unit mass transported is a given function c(x, y)onR 2d+2. Motivated by applications to shape recognition, we analyze this transportation problem with the cost c(x, y)=x−y  2 and measures supported on two curves in the plane, or more generally on the boundaries of two domains Ω,Λ ⊂ R d+1. Unlike the theory for measures which are absolutely continuous with respect to Lebesgue, it turns out not to be the case that µa.e. x ∈ ∂Ω is transported to a single image y ∈ ∂Λ; however, we show the images of x are almost surely collinear and parallel the normal to ∂Ω at x. If either domain is strictly convex, we deduce that the solution to the optimization problem is unique. When both domains are uniformly convex, we prove a regularity result showing the images of x ∈ ∂Ω are always collinear, and both images depend on x in a continuous and (continuously) invertible way. This produces some unusual extremal doubly stochastic measures. c○1998 by the authors. Reproduction of this article, in its entirety, is permitted for noncommercial purposes.
Shape Similarity Measures, Properties, and Constructions
 In Advances in Visual Information Systems, 4th International Conference, VISUAL 2000
, 2000
"... In this paper we list a number of similarity measures, some of which are not well known (such as the MongeKantorovich metric), or newly introduced (reflection metric). We formulate properties of similarity measures, and introduce new properties. We also give a set of constructions that have been us ..."
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Cited by 22 (1 self)
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In this paper we list a number of similarity measures, some of which are not well known (such as the MongeKantorovich metric), or newly introduced (reflection metric). We formulate properties of similarity measures, and introduce new properties. We also give a set of constructions that have been used in the design of some similarity measures, including some new constructions.