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A note on the top Lyapunov exponent of linear cooperative systems
In a recent paper [Asymptotic of the largest Floquet multiplier for cooperative matrices Annales de la Facult\'e des Sciences de Toulouse, Tome XXXI, no 4 (2022)] P. Carmona gives an asymptotic formulae for the top Lyapunov exponent of a linear T-periodic cooperative differential equation, in the limit T goes to infinity. This short note discusses and extends this result.
Supports of Invariant Measures for Piecewise Deterministic Markov Processes
For a class of piecewise deterministic Markov processes, the supports of the invariant measures are characterized. This is based on the analysis of controllability properties of an associated deterministic control system. Its invariant control sets determine the supports.
Regularity of the stationary density for systems with fast random switching
We consider the piecewise-deterministic Markov process obtained by randomly switching between the flows generated by a finite set of smooth vector fields on a compact set. We obtain H\"ormander-type conditions on the vector fields guaranteeing that the stationary density is: $C^k$ whenever the jump rates are sufficiently fast, for any $k<\infty$; unbounded whenever the jump rates are sufficiently slow and lower semi-continuous regardless of the jump rates. Our proofs are probabilistic, relying on a novel application of stopping times.
Smale Strategies for Network Prisoner's Dilemma Games
Smale's approach to the classical two-players repeated Prisoner's Dilemma game is revisited here for N -players and Network games in the framework of Blackwell's approachability, stochastic approximations and differential inclusions.
On strict convergence of stochastic gradients
We discuss conditions ensuring the (strict) convergence of stochastic gradient algorithms.
On Gradient like Properties of Population games, Learning models and Self Reinforced Processes
We consider ordinary differential equations on the unit simplex of $\RR^n$ that naturally occur in population games, models of learning and self reinforced random processes. Generalizing and relying on an idea introduced in \cite{DF11}, we provide conditions ensuring that these dynamics are gradient like and satisfy a suitable "angle condition". This is used to prove that omega limit sets and chain transitive sets (under certain smoothness assumptions) consist of equilibria; and that, in the real analytic case, every trajectory converges toward an equilibrium. In the reversible case, the dynamics are shown to be $C^1$ close to a gradient vector field. Properties of equilibria -with a special emphasis on potential games - and structural stability questions are also considered.