Geometric properties of cones with applications on the Hellinger-Kantorovich space, and a new distance on the space of probability measures

We study general geometric properties of cone spaces, and we apply them on the Hellinger--Kantorovich space $(\mathcal{M}(X),\mathsf{H\hspace{-0.25em} K}_{\alpha,\beta}).$ We exploit a two-parameter scaling property of the Hellinger-Kantorovich metric $\mathsf{H\hspace{-0.25em} K}_{\alpha,\beta},$ and we prove the existence of a distance $\mathsf{S\hspace{-0.18em} H\hspace{-0.25em} K}_{\alpha,\beta}$ on the space of Probability measures that turns the Hellinger--Kantorovich space $(\mathcal{M}(X),\mathsf{H\hspace{-0.25em} K}_{\alpha,\beta})$ into a cone space over the space of probabilities measures $(\mathcal{P}(X),\mathsf{S\hspace{-0.18em} H\hspace{-0.25em} K}_{\alpha,\beta}).$ We provide a two parameter rescaling of geodesics in $(\mathcal{M}(X),\mathsf{H\hspace{-0.25em} K}_{\alpha,\beta}),$ and for $(\mathcal{P}(X),\mathsf{S\hspace{-0.18em} H\hspace{-0.25em} K}_{\alpha,\beta})$ we obtain a full characterization of the geodesics. We finally prove finer geometric properties, including local-angle condition and partial $K$-semiconcavity of the squared distances, that will be used in a future paper to prove existence of gradient flows on both spaces

A Fenchel-Moreau-Rockafellar type theorem on the Kantorovich-Wasserstein space with Applications in Partially Observable Markov Decision Processes

By using the fact that the space of all probability measures with finite support can be somehow completed in two different fashions, one generating the Arens-Eells space and another generating the Kantorovich-Wasserstein (Wasserstein-1) space, and by exploiting the duality relationship between the Arens-Eells space with the space of Lipschitz functions, we provide a dual representation of Fenchel-Moreau-Rockafellar type for proper convex functionals on Wasserstein-1. We retrieve dual transportation inequalities as a Corollary and we provide examples where the theorem can be used to easily prove dual expressions like the celebrated Donsker-Varadhan variational formula. Finally our result allows to write convex functions as the supremum over all linear functions that are generated by roots of its conjugate dual, something that we apply to the field of Partially observable Markov decision processes (POMDPs) to approximate the value function of a given POMDP by iterating level sets. This extends the method used in Smallwood 1973 for finite state spaces to the case were the state space is a Polish metric space.Comment: 20 page

On a conjecture regarding the upper graph box dimension of bounded subsets of the real line

Let X \subset R be a bounded set; we introduce a formula that calculates the upper graph box dimension of X (i.e.the supremum of the upper box dimension of the graph over all uniformly continuous functions defined on X). We demonstrate the strength of the formula by calculating the upper graph box dimension for some sets and by giving an "one line" proof, alternative to the one given in [1], of the fact that if X has finitely many isolated points then its upper graph box dimension is equal to the upper box dimension plus one. Furthermore we construct a collection of sets X with infinitely many isolated points, having upper box dimension a taking values from zero to one while their graph box dimension takes any value in [max{2a,1},a + 1], answering this way, negatively to a conjecture posed in [1]

Generative adversarial learning of Sinkhorn algorithm initializations

The Sinkhorn algorithm [Cut13] is the state-of-the-art to compute approximations of optimal transport distances between discrete probability distributions, making use of an entropically regularized formulation of the problem. The algorithm is guaranteed to converge, no matter its initialization. This lead to little attention being paid to initializing it, and simple starting vectors like the n-dimensional one-vector are common choices. We train a neural network to compute initializations for the algorithm, which significantly outperform standard initializations. The network predicts a potential of the optimal transport dual problem, where training is conducted in an adversarial fashion using a second, generating network. The network is universal in the sense that it is able to generalize to any pair of distributions of fixed dimension. Furthermore, we show that for certain applications the network can be used independently

Entropic Gradient Flows on the Wasserstein Space via Large Deviations from Thermodynamic Limits

In a seminal work, Jordan, Kinderlehrer and Otto proved that the Fokker-Planck equation can be described as a gradient flow of the free energy functional in the Wasserstein space, bringing this way the statistical mechanics point of view on the diffusion phenomenon to the foreground. The aim of this thesis is to show that it is possible to retrieve this natural coupling of functional and metric, by studying the large deviations of particle models. More specically, for the case where the ambient space is the real line, it is proved that the free energy functional can be retrieved as an asymptotic Gamma-limit ( ! 0) of the rate function of a large deviation principle, minus the square of the Wasserstein distance (normalized by time). Furthermore, for a special case where both measures in the denition of the rate function are Gaussians, its value and the rate of convergence are being calculated explicitly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evolutionary variational inequalities on the Hellinger--Kantorovich and spherical Hellinger--Kantorovich spaces

We study the minimizing movement scheme for families of geodesically semiconvex functionals defined on either the Hellinger--Kantorovich or the Spherical Hellinger--Kantorovich space. By exploiting some of the finer geometric properties of those spaces, we prove that the sequence of curves, which are produced by geodesically interpolating the points generated by the minimizing movement scheme, converges to curves that satisfy the Evolutionary Variational Inequality (EVI), when the time step goes to 0

Entropic gradient flows on the Wasserstein space via large deviations from thermodynamic limits

In a seminal work, Jordan, Kinderlehrer and Otto proved that the Fokker-Planck equation can be described as a gradient flow of the free energy functional in the Wasserstein space, bringing this way the statistical mechanics point of view on the diffusion phenomenon to the foreground. The aim of this thesis is to show that it is possible to retrieve this natural coupling of functional and metric, by studying the large deviations of particle models. More specically, for the case where the ambient space is the real line, it is proved that the free energy functional can be retrieved as an asymptotic Gamma-limit ( ! 0) of the rate function of a large deviation principle, minus the square of the Wasserstein distance (normalized by time). Furthermore, for a special case where both measures in the denition of the rate function are Gaussians, its value and the rate of convergence are being calculated explicitly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo