Divide-and-conquer algorithms | Numerical linear algebra

Divide-and-conquer eigenvalue algorithm

Divide-and-conquer eigenvalue algorithms are a class of eigenvalue algorithms for Hermitian or real symmetric matrices that have recently (circa 1990s) become competitive in terms of stability and efficiency with more traditional algorithms such as the QR algorithm. The basic concept behind these algorithms is the divide-and-conquer approach from computer science. An eigenvalue problem is divided into two problems of roughly half the size, each of these are solved recursively, and the eigenvalues of the original problem are computed from the results of these smaller problems. Here we present the simplest version of a divide-and-conquer algorithm, similar to the one originally proposed by Cuppen in 1981. Many details that lie outside the scope of this article will be omitted; however, without considering these details, the algorithm is not fully stable. (Wikipedia).

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Rational function | Linear algebra | Diagonalizable matrix | Hermitian matrix | Big O notation | Parallel algorithm | Eigenvalue algorithm | Arnoldi iteration | Numerical stability | Recurrence relation | Real number | LAPACK | Symmetric matrix | Tridiagonal matrix | Computational complexity theory | QR algorithm | Rank (linear algebra) | Newton's method | Recursion | Master theorem (analysis of algorithms)