Generation of test matrices with specified eigenvalues using floating-point arithmetic

Katsuhisa Ozaki, Takeshi Ogita

Research output: Contribution to journalArticlepeer-review


This paper concerns test matrices for numerical linear algebra using an error-free transformation of floating-point arithmetic. For specified eigenvalues given by a user, we propose methods of generating a matrix whose eigenvalues are exactly known based on, for example, Schur or Jordan normal form and a block diagonal form. It is also possible to produce a real matrix with specified complex eigenvalues. Such test matrices with exactly known eigenvalues are useful for numerical algorithms in checking the accuracy of computed results. In particular, exact errors of eigenvalues can be monitored. To generate test matrices, we first propose an error-free transformation for the product of three matrices YSX. We approximate S by S to compute YSX without a rounding error. Next, the error-free transformation is applied to the generation of test matrices with exactly known eigenvalues. Note that the exactly known eigenvalues of the constructed matrix may differ from the anticipated given eigenvalues. Finally, numerical examples are introduced in checking the accuracy of numerical computations for symmetric and unsymmetric eigenvalue problems.

Original languageEnglish
Pages (from-to)241-262
Number of pages22
JournalNumerical Algorithms
Issue number1
Publication statusPublished - 2022 May


  • Eigenvalue problems
  • Floating-point arithmetic
  • Numerical linear algebra
  • Test matrices

ASJC Scopus subject areas

  • Applied Mathematics


Dive into the research topics of 'Generation of test matrices with specified eigenvalues using floating-point arithmetic'. Together they form a unique fingerprint.

Cite this