Rational Optimization for Nonlinear Reconstruction with Approximate $\ell_0$ Penalization

TL;DR

This paper introduces a global optimization framework for nonlinear signal reconstruction using rational approximation of the $ ext{l}_0$ pseudo-norm, employing semidefinite programming relaxations to handle sparsity and nonlinearity effectively.

Contribution

It develops a novel method combining rational approximation, semi-algebraic functions, and sparse semidefinite relaxations for globally optimal nonlinear signal reconstruction.

Findings

Effective handling of nonlinear models with rational saturation.

Scalable relaxation method for hundreds of variables.

Outperforms naive linearization approaches.

Abstract

Recovering nonlinearly degraded signal in the presence of noise is a challenging problem. In this work, this problem is tackled by minimizing the sum of a non convex 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 $\ell_0$ pseudo-norm thus constitutes a suitable penalization. The resulting composite cost function belongs to the broad class of semi-algebraic 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.