The Mather measure and a Large Deviation Principle for the Entropy Penalized Method
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We present a large deviation principle for the entropy penalized Mather problem when the Lagrangian L is generic (in this case the Mather measure $μ$ is unique and the support of $μ$ is the Aubry set). Consider, for each value of $ε$ and h, the entropy penalized Mather problem $\min \{\int_{\tn\times\rn}
L(x,v)dμ(x,v)+εS[μ]\},$ where the entropy S is given by $S[μ]=\int_{\tn\times\rn}μ(x,v)\ln\frac{μ(x,v)}{\int_{\rn}μ(x,w)dw}dxdv,$ and the minimization is performed over the space of probability densities $μ(x,v)$ that satisfy the holonomy constraint It follows from D. Gomes and E. Valdinoci that there exists a minimizing measure $μ_{ε, h}$ which converges to the Mather measure $μ$. We show a LDP $\lim_{ε,h\to0} ε\ln μ_{ε,h}(A),$ where $A\subset \mathbb{T}^N\times\mathbb{R}^N$. The deviation function I is given by $I(x,v)= L(x,v)+\nablaϕ_0(x)(v)-\bar{H}_{0},$ where $ϕ_0$ is the unique viscosity solution for L.