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MIT开发出来的计算光子晶体的软件

In this directory are datatypes and subroutines to handle matrix types
and operations common to iterative block eigensolver algorithms.  The
block eigensolver algorithm itself is implemented in eigensolver.c.

Such algorithms solve for the first p eigenstates of an n x n (sparse)
matrix A.  This corresponds to minimizing Yt*A*Y under the constraint
Yt*Y==1, where Y is an n x p matrix whose columns are the eigenvectors
and Yt is its adjoint.  The components of Y are, in general, complex
vectors of length c.

We define two datatypes:

evectmatrix -- An (N x c) x p matrix (row-major) whose columns are
               the eigenvects.  n = localN*c rows are stored local
               to the current process.

sqmatrix -- a p x p matrix.  These are typically used to hold things
            like Yt*Y.

The main reason for having two separate datatypes is that, on a
parallel machine, evectmatrix's are distributed while sqmatrix's
are not.  (In particular, evectmatrix's are distributed across
their rows.)

Various operations are defined on these matrices (multiplication, a X
+ Y, etcetera, typically using BLAS routines internally).  The
sqmatrix type also has operations like (dense) inversion and
diagonalization (based on LAPACK).  Together, these operations can be
used to easily implement most eigensolver algorithms.