# Numerical Analysis of Sparse Initial Data Identification for Parabolic Problems

## TL;DR

This paper develops a numerical method for identifying sparse initial data in parabolic equations from final observations, formulating it as a convex optimization problem with error estimates and efficient algorithms.

## Contribution

It introduces a novel sparse regularization approach for initial data identification in parabolic problems, with rigorous error analysis and practical algorithms.

## Key findings

- Control variable can be a finite sum of Dirac measures under structural assumptions.
- Error estimates for the locations and coefficients of Dirac measures are established.
- Numerical experiments validate the theoretical error bounds and algorithm efficiency.

## Abstract

In this paper we consider a problem of initial data identification from the final time observation for homogeneous parabolic problems. It is well-known that such problems are exponentially ill-posed due to the strong smoothing property of parabolic equations. We are interested in a situation when the initial data we intend to recover is known to be sparse, i.e. its support has Lebesgue measure zero. We formulate the problem as an optimal control problem and incorporate the information on the sparsity of the unknown initial data into the structure of the objective functional. In particular, we are looking for the control variable in the space of regular Borel measures and use the corresponding norm as a regularization term in the objective functional. This leads to a convex but non-smooth optimization problem. For the discretization we use continuous piecewise linear finite elements in space and discontinuous Galerkin finite elements of arbitrary degree in time. For the general case we establish error estimates for the state variable. Under a certain structural assumption, we show that the control variable consists of a finite linear combination of Dirac measures. For this case we obtain error estimates for the locations of Dirac measures as well as for the corresponding coefficients. The key to the numerical analysis are the sharp smoothing type pointwise finite element error estimates for homogeneous parabolic problems, which are of independent interest. Moreover, we discuss an efficient algorithmic approach to the problem and show several numerical experiments illustrating our theoretical results.