Fixed-point algorithms for frequency estimation and structured low rank approximation

TL;DR

This paper introduces fixed-point algorithms for structured low-rank matrix approximation, focusing on frequency estimation and sums of exponentials, with applications to missing data and multidimensional problems.

Contribution

It develops novel fixed-point algorithms that converge to convex envelopes and can handle weighted, multidimensional, and incomplete data scenarios.

Findings

Algorithms often achieve perfect reconstruction in missing data cases.

Convergence to the convex envelope is established for basic algorithms.

Extensions to multidimensional and curve-sampled data are demonstrated.

Abstract

We develop fixed-point algorithms for the approximation of structured matrices with rank penalties. In particular we use these fixed-point algorithms for making approximations by sums of exponentials, or frequency estimation. For the basic formulation of the fixed-point algorithm we show that it converges to the minimum of the convex envelope of the original objective function along with its structured matrix constraint. It often happens that this solution agrees with the solution to the original minimization problem, and we provide a simple criterium for when this is true. We also provide more general fixed-point algorithms that can be used to treat the problems of making weighted approximations by sums of exponentials given equally or unequally spaced sampling. We apply the method to the case of missing data, although optimal convergence of the fixed-point algorithm is not guaranteed in this case. However, it turns out that the method often gives perfect reconstruction (up to machine precision) in such cases. We also discuss multidimensional extensions, and illustrate how the proposed algorithms can be used to recover sums of exponentials in several variables, but when samples are available only along a curve.