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Large deviations principle by viscosity solutions: the case of diffusions with oblique Lipschitz reflections

Magdalena Kobylanski (2013)

Annales de l'I.H.P. Probabilités et statistiques

We establish a Large Deviations Principle for diffusions with Lipschitz continuous oblique reflections on regular domains. The rate functional is given as the value function of a control problem and is proved to be good. The proof is based on a viscosity solution approach. The idea consists in interpreting the probabilities as the solutions to some PDEs, make the logarithmic transform, pass to the limit, and then identify the action functional as the solution of the limiting equation.

Least regret control, virtual control and decomposition methods

Jacques-Louis Lions (2010)

ESAIM: Mathematical Modelling and Numerical Analysis

"Least regret control" consists in trying to find a control which "optimizes the situation" with the constraint of not making things too worse with respect to a known reference control, in presence of more or less significant perturbations. This notion was introduced in [7]. It is recalled on a simple example (an elliptic system, with distributed control and boundary perturbation) in Section 2. We show that the problem reduces to a standard optimal control problem for augmented state equations. On...

Lectures on maximal monotone operators.

R. R. Phelps (1997)

Extracta Mathematicae

These lectures will focus on those properties of maximal monotone operators which are valid in arbitrary real Banach spaces.

Limiti di problemi di Dirichlet nonlineari in domini variabili

Gianni Dal Maso, Anneliese Defranceschi (1987)

Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni

Si studia il comportamento limite di successioni di problemi variazionali nonlineari con condizioni al contorno di Dirichlet su aperti variabili. I principali strumenti usati in questa ricerca sono le nozioni di Γ -convergenza e di μ -capacità nonlineare.

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