arXiv subjects

Yurii Averboukh

Publications and source records attributed to Yurii Averboukh.

At least 19 recordsLinked to original sources

arXiv2026

Fokker-Planck-Kolmogorov inclusions of the mean field type

The paper studies a dynamical system in the Wasserstein space, where the evolution is governed by a Fokker-Planck-Kolmogorov equation with coefficients chosen from a prescribed set that depends at each point on that point and on the current measure. Under the assumption that the multivalued mapping determining the constraints is convex-valued, upper semicontinuous and satisfies certain growth conditions, we prove an existence theorem and establish the compactness of the solution set. Moreover, we study an optimal control problem for such dynamical systems.

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arXiv2025

Proximal aiming in weak KAM theory with nonsmooth Lagrangian

This work extends weak KAM theory to the case of a nonsmooth Lagrangian satisfying a superlinear growth condition. Using the solution of a weak KAM equation that is a stationary Hamilton-Jacobi equation and the proximal aiming method, we construct a family of discontinuous feedback strategies that are nearly optimal for every time interval. This result leads to an analogue of the weak KAM theorem. Additionally, as in classical weak KAM theory, we demonstrate that the effective Hamiltonian (Ma\~{n}\'{e} critical value) can be determined by solving a linear programming problem in the class of probability measures.

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arXiv2025

Upper and lower bounds of the value function for optimal control in the Wasserstein space

This paper explores the application of nonsmooth analysis in the Wasserstein space to finite-horizon optimal control problems for nonlocal continuity equations. We characterize the value function as a strict viscosity solution of the corresponding Bellman equation using the notions of $\varepsilon$-subdifferentials and $\varepsilon$-superdifferentials. The main paper's result is the fact that continuous subsolutions and supersolutions of this Bellman equation yield lower and upper bounds for the value function. These estimates rely on proximal calculus in the space of probability measures and the Moreau-Yosida regularization. Furthermore, the upper estimates provide a family of approximately optimal feedback strategies that realize the concept of proximal aiming.

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arXiv2025

Time-optimal problem in the space of probabilities measures

This paper focuses on the value function in the time-optimal problem for a continuity equation in the space of probability measures. We derive the dynamic programming principle for this problem. In particular, we prove that the Kruzhkov transform of the value function is a unique discontinuous viscosity solution to the corresponding Dirichlet problem for the Hamilton-Jacobi equation. Finally, we establish the $\Gamma$-convergence of the value function in a perturbed problem to the value function in the unperturbed problem.

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arXiv2024

Continuous time Markov chain based approximation of stationary and weak KAM Hamilton-Jacobi equations

Main objects of the paper are stationary and weak KAM Hamilton-Jacobi equations on the finite-dimensional torus. The key idea of the paper is to replace the underlying calculus of variations problems with continuous time Markov decision problems. This directly leads to an approximation of the stationary Hamilton-Jacobi equation by the Bellman equation for a discounting Markov decision problem. Developing elements of the weak KAM theory for the Markov decision problem, we obtain an approximation of the effective Hamiltonian. Additionally, convergences of the functional parts of the discrete weak KAM equations and Mather measures are shown. It turns out that the approximating equations are systems of algebraic equations. Thus, the paper's result can be seen as numerical schemes for stationary and weak KAM Hamilton-Jacobi equations.

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arXiv2024

Nonlocal balance equation: representation and approximation of solution

We study a nonlocal balance equation that describes the evolution of a system consisting of infinitely many identical particles those move along a deterministic dynamics and can also either disappear or give a spring. In this case, the solution of the balance equation is considered in the space of nonnegative measures. We prove the superposition principle for the examined nonlocal balance equation. Furthermore, we interpret the source/sink term as a probability rate of jumps from/to a remote point. Using this idea and replacing the deterministic dynamics of each particle by a nonlinear Markov chain, we approximate the solution of the balance equation by a solution of a system of ODEs and evaluate the corresponding approximation rate. This result can be used for construction of numerical solutions of the nonlocal balance equation.

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arXiv2023

Lipschitz Stability Estimate and Uniqueness in the Retrospective Analysis for The Mean Field Games System via Two Carleman Estimates

A retrospective analysis process for the mean field games system (MFGS) is considered. For the first time, Carleman estimates are applied to the analysis of the MFGS. Two new Carleman estimates are derived. They allow to obtain the Lipschitz stability estimate with respect to the possible error in the input initial and terminal data for a retrospective problem for MFGS. This stability estimate, in turn implies uniqueness theorem for the problem under the consideration. The idea of using Carleman estimates to obtain stability and uniqueness results came from the field of Ill-Posed and Inverse Problems.

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arXiv2023

Approximation of deterministic mean field type control systems

The paper is concerned with the approximation of the deterministic the mean field type control system by a mean field Markov chain. It turns out that the dynamics of the distribution in the approximating system is described by a system of ordinary differential equations. Given a strategy for the Markov chain, we explicitly construct a control in the deterministic mean field type control system. Our method is a realization of the model predictive approach. The converse construction is also presented. These results lead to an estimate of the Hausdorff distance between the bundles of motions in the deterministic mean field type control system and the mean field Markov chain. Especially, we pay the attention to the case when one can approximate the bundle of motions in the mean field type system by solutions of a finite systems of ODEs.

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arXiv2022

Stability analysis of mean field type control system with major agent

The paper is concerned with the study of a control system consisting of one major agent and many identical minor agents in the limit case when the number of agents tends to infinity. To study the limiting system we use the mean field approach. We first prove the existence and uniqueness of the motion for such system consisting of finite dimensional and mean field type subsystems. The main result of the paper is the stability of the motion w.r.t. perturbations of dynamics, initial data and controls. To illustrate the general theory, we examine the Stackelberg game where the leader controls the major agent while the follower affects the minor agents. Using the general theory, we show the existence of a solution.

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arXiv2022

Planning problem for continuous-time finite state mean field game with compact action space

The planning problem for the mean field game implies the one tries to transfer the system of infinitely many identical rational agents from the given distribution to the final one using the choice of the terminal payoff. It can be formulated as the mean field game system with the boundary condition only on the measure variable. In the paper, we consider the continuous-time finite state mean field game assuming that the space of actions for each player is compact. It is shown that the planning problem in this case may not admit a solution even if the final distribution is reachable from the initial one. Further, we introduce the concept of generalized solution of the planning problem for the finite state mean field game based on the minimization of regret of the representative player. This minimal regret solution always exists. Additionally, the set of minimal regret solution is the closure of the set of classical solution of the planning problem provided that the latter is nonempty.

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arXiv2022

Zero-sum continuous-time Markov games with one-side stopping

The paper is concerned with a variant of the continuous-time finite state Markov game of control and stopping where both players can affect transition rates, while only one player can choose a stopping time. We use the dynamic programming principle and reduce this problem to a system of ODEs with unilateral constraints. This system plays the role of the Bellman equation. We show that its solution provides the optimal strategies of the players. Additionally, we prove the existence and uniqueness theorem for the deduced system of ODEs with unilateral constraints.

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arXiv2022

Pontryagin maximum principle for the deterministic mean field type optimal control problem via the Lagrangian approach

We study necessary optimality conditions for the deterministic mean field type free-endpoint optimal control problem. Our study relies on the Lagrangian approach that treats the mean field type control system as a crowd of infinitely many agents who are labeled by elements of some probability space. First, we derive the Pontryagin maximum principle in the Lagrangian form. Furthermore, we consider the Kantorovich and Eulerian formalizations which describe mean field type control systems via distributions on the set of trajectories and nonlocal continuity equation respectively. We prove that local minimizers in the Kantorovich or Eulerian formulations determine local minimizers within the Lagrangian approach. Using this, we deduce the Pontryagin maximum principle in the Kantorovich and Eulerian forms. To illustrate the general theory, we examine a model system of mean field type linear quadratic regulator. We show that the optimal strategy in this case is determined by a linear feedback.

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arXiv2021

Control theory approach to continuous-time finite state mean field games

In the paper, we use the equivalent formulation of a finite state mean field game as a control problem with mixed constraints to study the dependence of solutions to finite state mean field game on an initial distribution of players. We introduce the concept of value multifunction of the mean field game that is a mapping assigning to an initial time and an initial distribution a set of expected outcomes of the representative player corresponding to solutions of the mean field game. Using the control reformulation of the finite state mean field game, we give the sufficient condition on a given multifunction to be a value multifunction in the terms of the viability theory. The maximal multifunction (i.e. the mapping assigning to an initial time and distribution the whole set of values corresponding to solutions of the mean field game) is characterized via the backward attainability set for the certain control systems.

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arXiv2020

Lattice approximations of the first-order mean field type differential games

The theory of first-order mean field type differential games examines the systems of infinitely many identical agents interacting via some external media under assumption that each agent is controlled by two players. We study the approximations of the value function of the first-order mean field type differential game using solutions of model finite-dimensional differential games. The model game appears as a mean field type continuous time Markov game, i.e., the game theoretical problem with the infinitely many agents and dynamics of each agent determined by a controlled finite state nonlinear Markov chain. Given a supersolution (resp. subsolution) of the Hamilton-Jacobi equation for the model game, we construct a suboptimal strategy of the first (resp. second) player and evaluate the approximation accuracy using the modulus of continuity of the reward function and the distance between the original and model games. This gives the approximations of the value function of the mean field type differential game by values of the finite-dimensional differential games. Furthermore, we present the way to build a finite-dimensional differential game that approximates the original game with a given accuracy.

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arXiv2019

A stability property in mean field type differential games

The paper is concerned with the feedback approach to the deterministic mean field type differential games. Previously, it was shown that suboptimal strategies in the mean field type differential game can constructed based on functions of time and probability satisfying the stability condition. This property realizes the dynamic programming principle for the constant control of one player. We present the infinitesimal form of this condition involving analogs of the directional derivatives. In particular, we obtain the characterization of the value function of the deterministic mean field type differential game in the terms of directional derivatives and the set of directions feasible by virtue of the dynamics of the game.

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arXiv2019

Viability analysis of the first-order mean field games

The paper is concerned with the dependence of the solution of the deterministic mean field game on the initial distribution of players. The main object of study is the mapping which assigns to the initial time and the initial distribution of players the set of expected rewards of the representative player corresponding to solutions of mean field game. This mapping can be regarded as a value multifunction. We obtain the sufficient condition for a multifunction to be a value multifunction. It states that if a multifunction is viable with respect to the dynamics generated by the original mean field game, then it is a value multifunction. Furthermore, the infinitesimal variant of this condition is derived.

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arXiv2018

Krasovskii-Subbotin approach to mean field type differential games

A mean field type differential game is a mathematical model of a large system of identical agents under mean-field interaction controlled by two players with opposite purposes. We study the case when the dynamics of each agent is given by ODE and the players can observe the distribution of the agents. We construct suboptimal strategies and prove the existence of the value function.

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arXiv2018

Approximate public-signal correlated equilibria for nonzero-sum differential games

We construct an approximate public-signal correlated equilibrium for a nonzero-sum differential game in the class of stochastic strategies with memory. The construction is based on a solution of an auxiliary nonzero-sum continuous-time stochastic game. This class of games includes stochastic differential games and continuous-time Markov games. Moreover, we study the limit of approximate equilibrium outcomes in the case when the auxiliary stochastic games tend to the original deterministic one. We show that it lies in the convex hull of the set of equilibrium values provided by deterministic punishment strategies.

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