mirror of
https://github.com/TREX-CoE/Sherman-Morrison.git
synced 2024-07-03 01:46:11 +02:00
152093dd7c
- Added a Verificarlo option to smvars.sh that sets the Verificarlo backend and uses LLVM for compilation. - Fixed residual_max() to give the Max-norm.
87 lines
3.1 KiB
Python
Executable File
87 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python
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import h5py
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import sys
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import numpy as np
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import math
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fname = sys.argv[1]
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dataset =h5py.File(fname, "r")
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cycle_num = sys.argv[2]
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cycle = "cycle_" + cycle_num
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cgroup = dataset[cycle]
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slater_matrix = cgroup["slater_matrix"][()]
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slater_inverse = cgroup["slater_inverse"][()]
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nupdates = cgroup["nupdates"][()]
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update_idxs = cgroup["col_update_index"][()]
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updates = cgroup["updates"][()]
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dim = cgroup["slater_matrix_dim"][()]
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Id = np.identity(dim)
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print(f'========================\n== UPDATE CYCLE: {cycle_num} ==\n========================')
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print()
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print(f'Number of updates: {nupdates}')
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print(f'Columns that need to be updated (replaced): {update_idxs}\n')
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# det_sm = np.linalg.det(slater_matrix)
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# det_si = np.linalg.det(slater_inverse)
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# eigen_values_sm, eigen_vectors_sm = np.linalg.eig(slater_matrix)
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# eigen_values_si, eigen_vectors_si = np.linalg.eig(slater_inverse)
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# SSi = np.matmul(slater_matrix, slater_inverse)
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# detSSi = np.linalg.det(SSi)
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# delta = Id - SSi
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# res_max = np.amax(abs(delta))
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# res_max2 = res_max*res_max
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# res_fro = np.linalg.norm(delta, 'fro')
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# res_fro2 = res_fro*res_fro
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# print(f'Determinant of Slater-matrix: {det_sm}')
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# print(f'Determinant of inverse Slater-matrix: {det_si}\n')
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# print(f'Smalles eigenvalue (modulus) of Slater-matrix: {np.amin(abs(eigen_values_sm))}')
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# print(f'Smalles eigenvalue (modulus) of inverse Slater-matrix: {np.amin(abs(eigen_values_si))}\n')
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# print(f'Det(S*Sinv): {detSSi}')
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# print(f'Residual (Id - S*Sinv) (Max norm): {res_max}')
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# print(f'Residual (Max norm sq.): {res_max2}')
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# print(f'Residual (Frob. norm): {res_fro}')
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# print(f'Residual (Frob. norm sq.): {res_fro2}\n')
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print(f'+-----------------------------------------------+\n| Updating Slater-matrix... |')
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slater_matrix_new = slater_matrix
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index = 0
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for update in updates:
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column = update_idxs[index] - 1
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slater_matrix_new[column] = update
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index = index + 1
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print(f'| Computing inverse of updated Slater-matrix... |\n+-----------------------------------------------+\n')
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slater_inverse_new = np.linalg.inv(slater_matrix_new)
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det_sm = np.linalg.det(slater_matrix_new)
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det_si = np.linalg.det(slater_inverse_new)
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eigen_values_sm, eigen_vectors_sm = np.linalg.eig(slater_matrix_new)
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eigen_values_si, eigen_vectors_si = np.linalg.eig(slater_inverse_new)
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SSi = np.matmul(slater_matrix_new, slater_inverse_new)
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detSSi = np.linalg.det(SSi)
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delta = Id - SSi
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res_max = np.amax(abs(delta))
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res_max2 = res_max*res_max
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res_fro = np.linalg.norm(delta, 'fro')
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res_fro2 = res_fro*res_fro
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print(f'(DUSM) Determinant of UPDATED Slater-matrix: {cycle_num}, {det_sm}')
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print(f'(DUSI) Determinant of UPDATED inverse Slater-matrix: {det_si}\n')
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print(f'(SEVUSM) Smalles eigenvalue (modulus) of UPDATED Slater-matrix: {np.amin(abs(eigen_values_sm))}')
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print(f'(SEVUSI) Smalles eigenvalue (modulus) of UPDATED inverse Slater-matrix: {np.amin(abs(eigen_values_si))}\n')
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print(f'(DSSI) det(S*Sinv): {detSSi}')
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print(f'(RMN) Residual (Id - S*Sinv) (Max norm): {res_max}')
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print(f'(RMN2) Residual (Max norm sq.): {res_max2}')
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print(f'(RFN) Residual (Frob. norm): {res_fro}')
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print(f'(RFN2) Residual (Frob. norm sq.): {res_fro2}\n')
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dataset.close() |