# Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems

## TL;DR

This paper develops a theoretical framework for sparse recovery in separable nonlinear inverse problems, extending convex programming methods beyond traditional compressed sensing assumptions, with applications in medical imaging and geophysics.

## Contribution

It introduces a new theory for sparse recovery in deterministic measurement settings, proving convex programming can successfully recover parameters under certain correlation conditions.

## Key findings

- Convex programming reliably recovers parameters when they are sufficiently distinct.
- The theory applies to deterministic measurement operators violating incoherence conditions.
- Numerical experiments demonstrate effectiveness in heat-source localization and brain activity estimation.

## Abstract

Extracting information from nonlinear measurements is a fundamental challenge in data analysis. In this work, we consider separable inverse problems, where the data are modeled as a linear combination of functions that depend nonlinearly on certain parameters of interest. These parameters may represent neuronal activity in a human brain, frequencies of electromagnetic waves, fluorescent probes in a cell, or magnetic relaxation times of biological tissues. Separable nonlinear inverse problems can be reformulated as underdetermined sparse-recovery problems, and solved using convex programming. This approach has had empirical success in a variety of domains, from geophysics to medical imaging, but lacks a theoretical justification. In particular, compressed-sensing theory does not apply, because the measurement operators are deterministic and violate incoherence conditions such as the restricted-isometry property. Our main contribution is a theory for sparse recovery adapted to deterministic settings. We show that convex programming succeeds in recovering the parameters of interest, as long as their values are sufficiently distinct with respect to the correlation structure of the measurement operator. The theoretical results are illustrated through numerical experiments for two applications: heat-source localization and estimation of brain activity from electroencephalography data.