The Inverse QR Decomposition in Order-recursive Calculation of Least Squares Coefficients

Daniel W. Apley, Jianjun Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we examine the properties of QR and inverse QR factorizations in the general linear least squares (LS) problem. By exploiting a straightforward geometric interpretation of the factorization, an efficient algorithm is derived that provides, order recursively, the LS coefficient vector, projection error vector, and residual error energy (i.e. the sum of the squares of the elements of the error vector) for all of the LS problems as the order varies from one to n, n being a prespecified maximum order. Using existing algorithms for time updating the inverse QR factorization, the method applies to the time recursive situation also. Given only R-l and the last row of Q in the inverse QR factorization of the data covariance matrix, all order updates of the LS coefficient vectors and residual error energies are carried out. Application to multichannel adaptive LS filtering is presented.
Original languageEnglish
Title of host publicationProceedings of the American Control Conference
StatePublished - 1995
EventAmerican Control Conference - Seattle, Washington
Duration: Jan 1 1995 → …

Conference

ConferenceAmerican Control Conference
Period1/1/95 → …

Fingerprint Dive into the research topics of 'The Inverse QR Decomposition in Order-recursive Calculation of Least Squares Coefficients'. Together they form a unique fingerprint.

Cite this