Changelog

1.2

  • Several functions to solve nearness problems (nearest symmetric matrix, nearest skew symmetric matrix, nearest positive semidefinite matrix, nearest correlation matrix) added.
  • Significant speedup of approximation algorithms in matrix.approximation.positive_semidefinite.Reimer.
  • Overflow handling improved in several functions.
  • Decompositions in matrix.decompositions now have an append_block_decomposition and as_same_type method and their mutiplication methods have now a dtype argument.
  • The approximation algorithms in matrix.approximation.positive_semidefinite.Reimer now have a min_abs_value_L argument.

1.1

  • Positive semidefinite approximation algorithms of GMW and SE type have been added.
  • Permutation method, with numerical stability as main focus, has been added to positive semidefinite approximation algorithm.
  • Positive semidefinite approximation algorithm are moved into separate package. (matrix.approximate to matrix.approximation.positive_semidefinite)

1.0.1

  • Approximation functions now also work if an overflows occurs.
  • NumPys matrix is avoided because it is deprecated now.

1.0

  • Approximation functions are slightly faster now.
  • Better overflow handling is now used in approximation functions.
  • Prebuild html documentation are now included.
  • Function for approximating a matrix by a positive semidefinite matrix (matrix.approximate.positive_semidefinite_matrix) has been removed.

0.8

  • Approximation functions have been replaced by more sophisticated approximation functions.
  • Explicit function for approximating a matrix by a positive (semi)definite matrix has been added.
  • Universal save and load functions have been added.
  • Decompositions have obtained is_equal and is_almost_equal methods.
  • Functions to multiply the matrix, represented by a decomposition, or its inverse with a matrix or a vector have been added.
  • It is now possible to pass permutation vectors to approximate and decompose methods.

0.7

  • Lineare systems associated to matrices or decompositions can now be solved.
  • Invertibility of matrices and decompositions can now be examined.
  • Decompositions can now be examined to see if they contain only finite values.

0.6

  • Decompositions are now saveable and loadable.

0.5

  • Matrices can now be approximated by decompositions.

0.4

  • Positive definiteness and positive semi-definiteness of matrices and decompositions can now be examined.

0.3

  • Dense and sparse matrices are now decomposable into several types (LL, LDL, LDL compressed).

0.2

  • Decompositons are now convertable to other decompositon types.
  • Decompositions are now comparable.

0.1

  • Several decompositions types (LL, LDL, LDL compressed) have been added.
  • Several permutation capabilities have been added.