Lyapunov instability of the equilibrium of the non-local continuity equation

The paper is devoted to developing Lyapunov's methods for analyzing the stability of an equilibrium of a dynamical system in the space of probability measures that is defined by a nonlocal continuity equation. Sufficient stability conditions are obtained based on the basis of an analysis of the behaviour of a nonsmooth Lyapunov function in a neighbourhood of the equilibrium and the investigation of a certain quadratic form defined on the tangent space of the space of probability measures. The general results are illustrated by the study of the stability of an equilibrium for a gradient flow in the space of probability measures and the Gibbs measure for a system of connected simple pendulums. Bibliography: 28 titles.

Let μ∈ P2(Rd), where P2(Rd) denotes the space of square integrable probability measures, and consider a Borel-measurable function Φ:P2(Rd)→R. In this paper we develop an antithetic Monte Carlo estimator (A-MLMC) for Φ(μ), which achieves sharp error bound under mild regularity assumptions. The estimator takes as input the empirical laws μN=1N∑ i=1NδX i, where (a) (Xi)i=1N is a sequence of i.i.d. samples from μ or (b) (Xi)i=1N is a system of interacting particles (diffusions) corresponding to a McKean–Vlasov stochastic differential equation (McKV-SDE). Each case requires a separate analysis. For a mean-field particle system, we also consider the empirical law induced by its Euler discretisation which gives a fully implementable algorithm. As by-products of our analysis, we establish a dimension-independent rate of uniform strong propagation of chaos, as well as an L2 estimate of the antithetic difference for i.i.d. random variables corresponding to general functionals defined on the space of probability measures.

The paper considers learning systems as optimisation systems with dynamical information constraints, and general optimality conditions are derived using the duality between the space of utility functions and probability measures. The increasing dynamics of the constraints is used to parametrise the optimal solutions which form a trajectory in the space of probability measures. Stochastic processes following such trajectories describe systems achieving the maximum possible utility gain with respect to a given information. The theory is discussed on examples for finite and uncountable sets and in relation to existing applications and cognitive models of learning.

Abstract. In this paper we investigate an optimal control problem in the space of measures on R2. The problem is motivated by a stochastic interacting particle model which gives the 2-D Navier-Stokes equations in their vorticity formulation as a mean-field equation. We prove that the associated HamiltonJacobi-Bellman equation, in the space of probability measures, is well posed in an appropriately defined viscosity solution sense.

We consider a class of non-local conservation laws with an interaction kernel supported on the negative real half-line and featuring a decreasing jump at the origin. We provide, for the first time, an existence and uniqueness theory for said model with initial data in the space of probability measures. Our concept of solution allows us to sort a lack of uniqueness problem which we exhibit in a specific example. Our approach uses the so-called quantile , or pseudo-inverse , formulation of the PDE, which has been largely used for similar types of non-local transport equations in one space dimension. Partly related to said approach, we then provide a deterministic particle approximation theorem for the equation under consideration, which works for general initial data in the space of probability measures with compact support. As a crucial step in both results, we use that our concept of solution (which we call dissipative measure solution ) implies an instantaneous measure-to- L^{\infty} smoothing effect, a property which is known also to be featured by local conservation laws with genuinely non-linear fluxes.

In this paper we formulate a time-optimal control problem in the space of probability measures. The main motivation is to face situations in finite-dimensional control systems evolving deterministically where the initial position of the controlled particle is not exactly known, but can be expressed by a probability measure on \(\mathbb {R}^{d}\). We propose for this problem a generalized version of some concepts from classical control theory in finite dimensional systems (namely, target set, dynamic, minimum time function...) and formulate an Hamilton-Jacobi-Bellman equation in the space of probability measures solved by the generalized minimum time function, by extending a concept of approximate viscosity sub/superdifferential in the space of probability measures, originally introduced by Cardaliaguet-Quincampoix in Cardaliaguet and Quincampoix (Int. Game Theor. Rev. 10, 1–16, 2008). We prove also some representation results linking the classical concept to the corresponding generalized ones. The main tool used is a superposition principle, proved by Ambrosio, Gigli and Savare in Ambrosio et al. [3], which provides a probabilistic representation of the solution of the continuity equation as a weighted superposition of absolutely continuous solutions of the characteristic system.

It is well known that the space of invariant probability measures for transitive sub-shifts of finite type is a Poulsen simplex. In this article we prove that in the non-compact setting, for a large family of transitive countable Markov shifts, the space of invariant sub-probability measures is a Poulsen simplex and that its extreme points are the ergodic invariant probability measures together with the zero measure. In particular, we obtain that the space of invariant probability measures is a Poulsen simplex minus a vertex and the corresponding convex combinations. Our results apply to finite entropy non-locally compact transitive countable Markov shifts and to every locally compact transitive countable Markov shift. In order to prove these results we introduce a topology on the space of measures that generalizes the vague topology to a class of non-locally compact spaces, the topology of convergence on cylinders. We also prove analogous results for suspension flows defined over countable Markov shifts.

We study well-posedness of a class of nonlocal interaction equations with spatially dependent mobility. We also allow for the presence of boundaries and external potentials. Such systems lead to the study of nonlocal interaction equations on subsets ℳ of ℝ d endowed with a Riemannian metric g. We obtain conditions, relating the interaction potential and the geometry, which imply existence, uniqueness and stability of solutions. We study the equations in the setting of gradient flows in the space of probability measures on ℳ endowed with Riemannian 2-Wasserstein metric.

Spaces of probability measures with positive density function on a compact\nRiemannian manifold are endowed with a closed 2-form associated with the Fisher\ninformation metric by using a divergence-free vector field. In this note we give a\nnecessary and sufficient condition on the vector field that this 2-form is a\nstrong symplectic structure.

A definition of Lévy processes with values in the space of probability measures was introduced by Shiga and Tanaka (Electronic J. Prob. 11 (2006)). It is shown that the Lévy process with values in the space of probability measures in law has a modification satisfying a certain condition. The modification is a Lévy process in the sense of Shiga and Tanaka. The Lévy-Itô decomposition of sample paths of the Lévy process satisfying the condition is derived.

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

A sufficient condition for the linear instability of strain-rate-dependent solids

A Sufficient Condition for Instability in the Limit of Vanishing Dissipation

It is shown that for most, but not all, three-dimensional magnetohydrodynamic (MHD) equilibria the second variation of the energy is indefinite. Thus the class of such equilibria whose stability might be determined by the so-called Arnold criterion is very restricted. The converse question, namely conditions under which MHD equilibria will be unstable is considered in this paper. The following sufficient condition for linear instability in the Eulerian representation is presented: The maximal real part of the spectrum of the MHD equations linearized about an equilibrium state is bounded from below by the growth rate of an operator defined by a system of local partial differential equations (PDE). This instability criterion is applied to the case of axisymmetric toroidal equilibria. Sufficient conditions for instability, stronger than those previously known, are obtained for rotating MHD. (c) 1995 American Institute of Physics.

In this short communication we prove that the subspace Pn,n−1(X)of all probability measures P(X), whose supports consist of exactly n points is an (n−1)-dimensional topological manifold. A number of subspaces of the space of all probability measures having infinite dimension in the sense of dim, which are manifolds, are identified. We also consider individual subsets of the infinite compact set X, on which the space of probability measures is homotopy dense in the entire space. Three theorems on the topological properties of manifolds—subspaces of homotopy dense probability measures in the space of probability measures with finite supports on a compactum—are formulated and proven, and special cases of finite and infinite compactums are considered.