1. 07 Nov, 2017 2 commits
    • Pavel Kus's avatar
      generalized eigenvector problem progress · 108129a3
      Pavel Kus authored
    • Pavel Kus's avatar
      fixing Cholesky decomposition test · 85af2858
      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.
  2. 30 Oct, 2017 1 commit
  3. 25 Oct, 2017 3 commits
  4. 11 Sep, 2017 2 commits
  5. 10 Sep, 2017 1 commit
  6. 09 Sep, 2017 2 commits
  7. 03 Sep, 2017 2 commits
  8. 01 Sep, 2017 2 commits
  9. 24 Aug, 2017 1 commit
  10. 21 Aug, 2017 2 commits
  11. 18 Aug, 2017 2 commits
  12. 17 Aug, 2017 3 commits
  13. 10 Aug, 2017 2 commits
  14. 05 Aug, 2017 1 commit
  15. 03 Aug, 2017 1 commit
  16. 01 Aug, 2017 1 commit
  17. 31 Jul, 2017 1 commit
  18. 30 Jul, 2017 1 commit
    • Lorenz Huedepohl's avatar
      Loop over all possible domain decompositions · fabb1c42
      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 column-major 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.
  19. 29 Jul, 2017 1 commit
  20. 27 Jul, 2017 1 commit
  21. 25 Jul, 2017 1 commit
  22. 20 Jul, 2017 1 commit
    • Pavel Kus's avatar
      extending check_correctness · dbef90e4
      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).
  23. 18 Jul, 2017 5 commits
  24. 17 Jul, 2017 1 commit
    • Pavel Kus's avatar
      Introducing analytical test · 8a9c9df1
      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
      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.