Rational Optimization for Nonlinear Reconstruction With Approximate [Formula Omitted] Penalization

Recovering nonlinearly degraded signal in the presence of noise is a challenging problem. In this paper, this problem is tackled by minimizing the sum of a nonconvex least-squares fit criterion and a penalty term. We assume that the nonlinearity of the model can be accounted for by a rational function. In addition, we suppose that the signal to be sought is sparse and a rational approximation of the [Formula Omitted] pseudonorm thus constitutes a suitable penalization. The resulting composite cost function belongs to the broad class of semialgebraic functions. To find a globally optimal solution to such an optimization problem, it can be transformed into a generalized moment problem, for which a hierarchy of semidefinite programming relaxations can be built. Global optimality comes at the expense of an increased dimension and to overcome computational limitations concerning the number of involved variables, the structure of the problem has to be carefully addressed. A situation of practical interest is when the nonlinear model consists of a convolutive transform followed by a componentwise nonlinear rational saturation. We then propose to use a sparse relaxation able to deal with up to several hundreds of optimized variables. In contrast with the naive approach consisting of linearizing the model, our experiments show that the proposed approach offers good performance.