- source file: conds.py
- tests:
- m1, 1, -1
- m2, 1, 1
- m3, 1, -1
- m4, 0, 0
- hilbert, 3, -1
- conclusions:
- m1, m3, hilbert => grand conds lead to grand errors
- m2 => method inaplicable for identity matrix
- m4 => method inaplicable for some tridiagonal matrix
- source files:
- lu_decomposition.py
- regularisation.py
- conclusions:
- conds change during LU decomposition
- best alphas in regularisation do not match
- source file: qr_decomposition.py
- conclusion: sometimes cond_s grow up, but other conds always go down
- source file: eigenvalue.py
- conclusions:
- the less is the epsilon the more iterations are needed
- scal_method is better
- source file: jacobi.py
- conclusions:
- The more is precision, the more iterations are needed.
- Found eigenvalues are in Gershgorin circles.
- source file: grid_method.py
- conclusions are obvious
- source file: galerkin.py
- conclusion: the bigger is N the better is solution
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