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### Symmetric Eigenproblems (SEP)

The symmetric eigenvalue problem is to find the eigenvalues, , and corresponding eigenvectors, , such that

For the Hermitian eigenvalue problem we have

For both problems the eigenvalues are real.

When all eigenvalues and eigenvectors have been computed, we write:

where is a diagonal matrix whose diagonal elements are the eigenvalues, and is an orthogonal (or unitary) matrix whose columns are the eigenvectors. This is the classical spectral factorization of .
There are four types of driver routines for symmetric and Hermitian eigenproblems, and these are listed below. Originally LAPACK had just the first two (the simple and expert drivers), and the last two were added after improved algorithms were discovered. Ultimately we expect the algorithm in the most recent driver (called RRR below) to supersede all the others, but in LAPACK 3.0 the other drivers may still be faster on some problems, so we retain them.
• A simple driver (name ending -EV) computes all the eigenvalues and (optionally) eigenvectors.
• An expert driver (name ending -EVX) computes all or a selected subset of the eigenvalues and (optionally) eigenvectors. If few enough eigenvalues or eigenvectors are desired, the expert driver is faster than the simple driver.
• A divide-and-conquer driver (name ending -EVD) solves the same problem as the simple driver. It is much faster than the simple driver for large matrices, but uses more workspace. The name divide-and-conquer refers to the underlying algorithm (see sections 2.4.4 and 3.4.3 in the LAPACK Users' Guide[1]).
• A relatively robust representation (RRR) driver (name ending -EVR) computes all or (in a later release) a subset of the eigenvalues, and (optionally) eigenvectors. It is the fastest algorithm of all (except for a few cases), and uses the least workspace. The name RRR refers to the underlying algorithm (see sections 2.4.4 and 3.4.3 in the LAPACK Users' Guide[1]).
Different driver routines are provided to take advantage of special structure or storage of the matrix , as shown in Table 2.5.

Next: Nonsymmetric Eigenproblems (NEP) Up: Standard Eigenvalue and Singular Previous: Standard Eigenvalue and Singular   Contents   Index
Susan Blackford 2001-08-19