The Mather measure and a Large Deviation Principle for the Entropy Penalized Method

We present a large deviation principle for the entropy penalized Mather problem when the Lagrangian L is generic (in this case the Mather measure $\mu$ is unique and the support of $\mu$ is the Aubry set). Consider, for each value of $\epsilon$ and h, the entropy penalized Mather problem $\min \{\int_{\tn\times\rn} L(x,v)d\mu(x,v)+\epsilon S[\mu]\},$ where the entropy S is given by $S[\mu]=\int_{\tn\times\rn}\mu(x,v)\ln\frac{\mu(x,v)}{\int_{\rn}\mu(x,w)dw}dxdv,$ and the minimization is performed over the space of probability densities $\mu(x,v)$ that satisfy the holonomy constraint It follows from D. Gomes and E. Valdinoci that there exists a minimizing measure $\mu_{\epsilon, h}$ which converges to the Mather measure $\mu$. We show a LDP $\lim_{\epsilon,h\to0} \epsilon \ln \mu_{\epsilon,h}(A),$ where $A\subset \mathbb{T}^N\times\mathbb{R}^N$. The deviation function I is given by $I(x,v)= L(x,v)+\nabla\phi_0(x)(v)-\bar{H}_{0},$ where $\phi_0$ is the unique viscosity solution for L.