The Eigenspace Spectral Regularization Method for Solving Discrete Ill‐Posed Systems

Fredrick Asenso Wireko, Benedict Barnes, Charles Sebil and Joseph Ackora-Prah

Journal of Applied Mathematics, 2021, vol. 2021, issue 1

Abstract: This paper shows that discrete linear equations with Hilbert matrix operator, circulant matrix operator, conference matrix operator, banded matrix operator, TST matrix operator, and sparse matrix operator are ill‐posed in the sense of Hadamard. Gauss least square method (GLSM), QR factorization method (QRFM), Cholesky decomposition method (CDM), and singular value decomposition (SVDM) failed to regularize these ill‐posed problems. This paper introduces the eigenspace spectral regularization method (ESRM), which solves ill‐posed discrete equations with Hilbert matrix operator, circulant matrix operator, conference matrix operator, and banded and sparse matrix operator. Unlike GLSM, QRFM, CDM, and SVDM, the ESRM regularizes such a system. In addition, the ESRM has a unique property, the norm of the eigenspace spectral matrix operator κ(K) = ‖K−1K‖ = 1. Thus, the condition number of ESRM is bounded by unity, unlike the other regularization methods such as SVDM, GLSM, CDM, and QRFM.

Date: 2021
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https://doi.org/10.1155/2021/4373290

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2021:y:2021:i:1:n:4373290

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