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- Approximation functions have been replaced by more sophisticated approximation functions.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.