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We prove a refined contraction inequality for diffusion semigroups with respect to the Wasserstein distance on a compact Riemannian manifold taking account of the dimension. The result generalizes in a Riemannian context, the dimensional…

Probability · Mathematics 2014-12-16 Ivan Gentil

We consider a controlled state equation of parabolic type on the halfline $(0,+\infty)$ with boundary conditions of Dirichlet type in which the unknown is equal to the sum of the control and of a white noise in time. We study finite horizon…

Probability · Mathematics 2013-04-10 Federica Masiero

We introduce in this paper a strategy to prove gradient estimates for some infinite-dimensional diffusions on $L_2$-Wasserstein spaces. For a specific example of a diffusion on the $L_2$-Wasserstein space of the torus, we get a…

Probability · Mathematics 2021-07-23 Victor Marx

It is now well known that curvature conditions \`a la Bakry-Emery are equivalent to contraction properties of the heat semigroup with respect to the classical quadratic Wasserstein distance. However, this curvature condition may include a…

Probability · Mathematics 2017-05-17 François Bolley , Ivan Gentil , Arnaud Guillin

In this article we provide Bernstein type gradient estimates for two system of local weighted heat type equations with potentials on a weighted Riemannian manifold. We derive all possible cases considering linear potential, exponential…

Analysis of PDEs · Mathematics 2026-01-21 Sujit Bhattacharyya

We consider gradient estimates to positive solutions of porous medium equations and fast diffusion equations: $$u_t=\Delta_\phi(u^p)$$ associated with the Witten Laplacian on Riemannian manifolds. Under the assumption that the…

Differential Geometry · Mathematics 2012-03-27 Guangyue Huang , Haizhong Li

We consider large deviations of empirical measures of diffusion processes. In a first part, we present conditions to obtain a large deviations principle (LDP) for a precise class of unbounded functions. This provides an analogue to the…

Probability · Mathematics 2020-09-23 Grégoire Ferré , Gabriel Stoltz

We study distributionally robust optimization (DRO) problems with uncertainty sets consisting of high-dimensional random vectors that are close in the multivariate Wasserstein distance to a reference random vector. We give conditions when…

Optimization and Control · Mathematics 2026-01-30 Brandon Tam , Silvana M. Pesenti

We study the boundary control problems for the wave, heat, and Schr\"odinger equations on a finite graph. We suppose that the graph is a tree (i.e., it does not contain cycles), and on each edge an equation is defined. The control is acting…

Optimization and Control · Mathematics 2025-05-28 S. A. Avdonin , V. S. Mikhaylov

In this paper, we study the control of the linear heat equation with a space and time dependent coefficient function by the Dirichlet and Neumann boundary control laws. This equation models the heat diffusion and space, time dependent heat…

Optimization and Control · Mathematics 2007-05-23 Junji Jia

We construct a recurrent diffusion process with values in the space of probability measures over an arbitrary closed Riemannian manifold of dimension $d\ge 2$. The process is associated with the Dirichlet form defined by integration of the…

Probability · Mathematics 2022-04-04 L. Dello Schiavo

We present a novel computational framework for density control in high-dimensional state spaces. The considered dynamical system consists of a large number of indistinguishable agents whose behaviors can be collectively modeled as a…

Optimization and Control · Mathematics 2023-07-26 Shaojun Ma , Mengxue Hou , Xiaojing Ye , Haomin Zhou

In the paper, the problems of approximate controllability are studied for the control system $w_t=\Delta w$, $w(0,x_2,t)=u(x_2,t)$, $x_1\in\mathbb R_+=(0,+\infty)$, $x_2\in\mathbb R$, $t\in(0,T)$, where $u$ is a control belonging to a…

Optimization and Control · Mathematics 2025-06-13 Larissa Fardigola , Kateryna Khalina

We survey recent results on the control problem for the heat equation on unbounded and large bounded domains. First we formulate new uncertainty relations, respectively spectral inequalities. Then we present an abstract control cost…

Analysis of PDEs · Mathematics 2020-08-18 Michela Egidi , Ivica Nakić , Albrecht Seelmann , Matthias Täufer , Martin Tautenhahn , Ivan Veselic

In this paper we consider an optimal control problem governed by a semilinear heat equation with bilinear control-state terms and subject to control and state constraints. The state constraints are of integral type, the integral being with…

Optimization and Control · Mathematics 2020-09-16 M. Soledad Aronna , J. Frédéric Bonnans , Axel Kröner

Let G = (V, p, $\mu$) be a (finite or infinite) weighted graph with bounded geometry. Assuming that G satisfies the classical curvaturedimension condition of Bakry-Emery CD(K, n) with K $\ge$ 0 (for the usual Laplacian), we prove that the…

Differential Geometry · Mathematics 2025-07-28 Emmanuel Russ , Hervé Pajot

We present a method for the numerical approximation of distributed optimal control problems constrained by parabolic partial differential equations. We complement the first-order optimality condition by a recently developed space-time…

Numerical Analysis · Mathematics 2022-08-23 Thomas Führer , Michael Karkulik

Upper bounds are obtained for the heat content of an open set D in a geodesically complete Riemannian manifold M with Dirichlet boundary condition on bd(D), and non-negative initial condition. We show that these upper bounds are close to…

Spectral Theory · Mathematics 2011-06-03 M. van den Berg , P. Gilkey , K. Kirsten , A. Grigor'yan

For incomplete sub-Riemannian manifolds, and for an associated second-order hypoelliptic operator, which need not be symmetric, we identify two alternative conditions for the validity of Gaussian-type upper bounds on heat kernels and…

Probability · Mathematics 2022-03-23 Ismael Bailleul , James Norris

Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence. We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or…

Machine Learning · Computer Science 2017-12-21 Pierre H. Richemond , Brendan Maginnis