 07 Nov, 2017 1 commit


Pavel Kus authored
This test was wrong, it was computing A * A^T instead of A^T * A. The latter is correct since our implementation of Cholesky decomposition stores the triangular matrix in the upper triangle The test was passing only because the Cholesky decomposition was tested with diagonal matrix only, than this does not matter.

 30 Oct, 2017 1 commit


Pavel Kus authored

 25 Oct, 2017 3 commits
 11 Sep, 2017 2 commits


Andreas Marek authored

Andreas Marek authored
But allow this in cholesky and hermitian multiply test

 10 Sep, 2017 1 commit


Andreas Marek authored

 09 Sep, 2017 2 commits


Andreas Marek authored

Andreas Marek authored
It is planned to add another matrix type for the tests. The names of the prepare routines have become a bit inconsistent and confusing. Thus the rename

 03 Sep, 2017 2 commits
 01 Sep, 2017 2 commits
 24 Aug, 2017 1 commit


Andreas Marek authored

 21 Aug, 2017 2 commits


Andreas Marek authored

Andreas Marek authored

 18 Aug, 2017 2 commits


Andreas Marek authored

Andreas Marek authored

 17 Aug, 2017 3 commits


Andreas Marek authored

Andreas Marek authored

Andreas Marek authored

 10 Aug, 2017 2 commits


Lorenz Huedepohl authored
with obvious meaning

Pavel Kus authored
for easier comparisons of elpa and mkl, a test case using scalapack function pdsyevd has been added

 05 Aug, 2017 1 commit


Andreas Marek authored

 03 Aug, 2017 1 commit


Andreas Marek authored

 01 Aug, 2017 1 commit


Andreas Marek authored

 31 Jul, 2017 1 commit


Lorenz Huedepohl authored

 30 Jul, 2017 1 commit


Lorenz Huedepohl authored
We got reports from a user that there were troubles with certain domain decompositions. So far the tests only looked at (approximately) square decompositions in columnmajor process order. Now, a new class of tests loops over all possible decompositions (row * col) for a given number of total tasks. So far, we can not confirm that there are any problems, all possibilities work as expected.

 29 Jul, 2017 1 commit


Pavel Kus authored
matrices of dimension of the form 2^n * 3^m * 5^m are now allowed. For other matrix sizes, the test is terminated. Matrix size is not modified, as it has been the case before

 27 Jul, 2017 1 commit


Andreas Marek authored

 25 Jul, 2017 1 commit


Lorenz Huedepohl authored

 20 Jul, 2017 1 commit


Pavel Kus authored
Previously we checked ortonormality of eigenvectors by comparing the matrix S^T * S to identity matrix. The new feature is also checking just the diagonal of the matrix S^T * S. By that we get the information, how far are the eigenvectors from having length 1. If it turns out to be important, we could try to normalize them at the end of elpa, which is simple (in contrast with enforcing better orthogonality).

 18 Jul, 2017 5 commits


Andreas Marek authored

Andreas Marek authored

Andreas Marek authored

Andreas Marek authored

Andreas Marek authored
The functions in elpa_utilities are considered "ELPA internal", i.e. the should not be accessible by the users and thus not be part of the API.

 17 Jul, 2017 2 commits


Pavel Kus authored
Introducing new test in which matrix and its eigendecomposition is known and thus can be easily created and checked directly, without the need to use scalapack or any other communication (apart from reducing error). The test is based on the fact, that if L_A and S_A are eigenvalues and eigenvectors of matrix A, respectively, and L_B and S_B eigenvalues and eigenvectors of B, then kron(L_A, L_B) and kron (S_A, S_B) are eigenvalues and eigenvectors of kron(A, B). Since it is easy to know exact eigendecomposition of a small matrix (e.g. 2x2), and kron operator has very simple structure, we can construct arbitrarily large matrix and its eigendecomposition. We only have to select small matrices such that the resulting matrix has unique and ordered eigenvalues, so that the checking of the result is than easy. Each element of matrix, eigenvector matrix and eigenvalue vector can be quickly computed independently, just using its global coordinates. The test is currently limited to matrices of size 2^n, but by storing eigendecompositions of more small matrices (e.g. 3x3 and 5x5) we could construct any matrix of size 2^n*3^m*5^o, which would probably be sufficient, since most often used sizes (150, 1000, 5000, 2000, 60000) are of this form.

Andreas Marek authored
The routines "eigenvalues" and "solve_tridiagonal" are now also tested directly with the new API (and not only via the legacy API > new API mapping) Todo: adapt test generator script to contain some logic
