This book focuses on the interpretation of ergodic optimal problems as questions of variational dynamics, employing a comparable approach to that of the Aubry-Mather theory for Lagrangian systems. Ergodic optimization is primarily concerned with the study of optimizing probability measures. This work presents and discusses the fundamental concepts of the theory, including the use and relevance of Sub-actions as analogues to subsolutions of the Hamilton-Jacobi equation. Further, it provides evidence for the impressively broad applicability of the tools inspired by the weak KAM theory
The foundation of statistical mechanics and the explanation of the success of its methods rest on th...
31 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1956.U of I OnlyRestricted to the U...
This important book reviews applications of optimization and optimal control theory to modern proble...
Let f be a real-valued function defined on the phase space of a dynamical system. Ergodic optimizati...
Ergodic optimization is the study of problems relating to maximizing orbits and invariant measures, ...
Sub-actions can be interpreted as a concept which corresponds by duality to maximizing probabilities...
We propose a new model of ergodic optimization for expanding dynamical systems: the holonomic settin...
Ergodic optimization and discrete weak KAM theory are two parallel theories with several results in ...
We show a connection between global unconstrained optimization of a continuous function f and weak K...
Subgradient methods are popular tools for nonsmooth, convex minimization, especially in the context ...
Subgradient methods are popular tools for nonsmooth, convex minimization, especially in the context ...
The problem of learning from data is prevalent in the modern scientific age, and optimization provid...
We consider maximizing orbits and maximizing measures for continuous maps T : X ! X and functions f...
We adapt the metric approach to the study of stationary ergodic Hamilton-Jacobi equations, for which...
The purpose of this thesis is to shed some light on the long time behavior of potential Mean Field G...
The foundation of statistical mechanics and the explanation of the success of its methods rest on th...
31 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1956.U of I OnlyRestricted to the U...
This important book reviews applications of optimization and optimal control theory to modern proble...
Let f be a real-valued function defined on the phase space of a dynamical system. Ergodic optimizati...
Ergodic optimization is the study of problems relating to maximizing orbits and invariant measures, ...
Sub-actions can be interpreted as a concept which corresponds by duality to maximizing probabilities...
We propose a new model of ergodic optimization for expanding dynamical systems: the holonomic settin...
Ergodic optimization and discrete weak KAM theory are two parallel theories with several results in ...
We show a connection between global unconstrained optimization of a continuous function f and weak K...
Subgradient methods are popular tools for nonsmooth, convex minimization, especially in the context ...
Subgradient methods are popular tools for nonsmooth, convex minimization, especially in the context ...
The problem of learning from data is prevalent in the modern scientific age, and optimization provid...
We consider maximizing orbits and maximizing measures for continuous maps T : X ! X and functions f...
We adapt the metric approach to the study of stationary ergodic Hamilton-Jacobi equations, for which...
The purpose of this thesis is to shed some light on the long time behavior of potential Mean Field G...
The foundation of statistical mechanics and the explanation of the success of its methods rest on th...
31 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1956.U of I OnlyRestricted to the U...
This important book reviews applications of optimization and optimal control theory to modern proble...