Fast Structured Total Least Squares Algorithm for Solving the Basic Deconvolution Problem

Nicola Mastronardi, Philippe Lemmerling, Sabine Van Huffel

SIAM Journal on Matrix Analysis and Applications · 2000 · 77 citations · 26 references

Concepts

Abstract

In this paper we develop a fast algorithm for the basic deconvolution problem. First we show that the kernel problem to be solved in the basic deconvolution problem is a so-called structured Total Least Squares problem. Due to the low displacement rank of the involved matrices, we are able to develop a fast algorithm. We apply the new algorithm on a deconvolution problem arising in a medical application in renography. By means of this example, we show the increased computational performance of our algorithm as compared to other algorithms for solving this type of structured Total Least Squares problems. In addition, Monte-Carlo simulations indicate the superior statistical performance of the structured Total Least Squares estimator compared to other estimators such as the ordinary Total Least Squares estimator. FAST STRUCTURED TOTAL LEAST SQUARES ALGORITHM FOR SOLVING THE BASIC DECONVOLUTION PROBLEM NICOLA MASTRONARDI y , PHILIPPE LEMMERLING z , AND SABINE VAN HUFFEL x Abstrac...

References

26