1#
2#   CheMPS2: a spin-adapted implementation of DMRG for ab initio quantum chemistry
3#   Copyright (C) 2013-2018 Sebastian Wouters
4#
5#   This program is free software; you can redistribute it and/or modify
6#   it under the terms of the GNU General Public License as published by
7#   the Free Software Foundation; either version 2 of the License, or
8#   (at your option) any later version.
9#
10#   This program is distributed in the hope that it will be useful,
11#   but WITHOUT ANY WARRANTY; without even the implied warranty of
12#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
13#   GNU General Public License for more details.
14#
15#   You should have received a copy of the GNU General Public License along
16#   with this program; if not, write to the Free Software Foundation, Inc.,
17#   51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
18#
19
20import numpy as np
21import sys
22import PyCheMPS2
23import ctypes
24
25# Set the seed of the random number generator and cout.precision
26Initializer = PyCheMPS2.PyInitialize()
27Initializer.Init()
28
29########################
30### 1D Hubbard model ###
31########################
32
33L = 10       # Number of lattice sites
34Group = 0    # C1 symmetry
35U = 2.0      # On-site repulsion
36T = -1.0     # Hopping term
37
38TwoS  = 5    # Two times the targeted spin
39Nelec = 9    # The number of electrons
40Irrep = 0    # The targeted irrep
41
42# The Hamiltonian initializes all its matrix elements to 0.0
43orbirreps = np.zeros([L], dtype=ctypes.c_int)
44Ham = PyCheMPS2.PyHamiltonian(L, Group, orbirreps)
45for cnt in range(0, L):
46    Ham.setVmat(cnt, cnt, cnt, cnt, U)
47for cnt in range(0, L-1):
48    Ham.setTmat(cnt, cnt+1, T)
49
50# Setting up the Problem
51Prob = PyCheMPS2.PyProblem(Ham, TwoS, Nelec, Irrep)
52
53# Setting up the ConvergenceScheme
54# setInstruction(instruction, D, Econst, maxSweeps, noisePrefactor)
55OptScheme = PyCheMPS2.PyConvergenceScheme(2) # 2 instructions
56OptScheme.setInstruction(0,   30, 1e-10,  3, 0.1)
57OptScheme.setInstruction(1, 1000, 1e-10, 10, 0.0)
58
59# Do DMRG calculation and print the correlations
60theDMRG = PyCheMPS2.PyDMRG(Prob, OptScheme)
61EnergyDMRG = theDMRG.Solve()
62theDMRG.calc2DMandCorrelations()
63theDMRG.printCorrelations()
64
65# Clean-up
66# theDMRG.deleteStoredMPS()
67theDMRG.deleteStoredOperators()
68del theDMRG
69del OptScheme
70del Prob
71
72# Do FCI calculation
73Nel_up   = ( Nelec + TwoS ) / 2
74Nel_down = ( Nelec - TwoS ) / 2
75maxMemWorkMB = 10.0
76FCIverbose = 1
77theFCI = PyCheMPS2.PyFCI(Ham, Nel_up, Nel_down, Irrep, maxMemWorkMB, FCIverbose)
78GSvector = np.zeros([ theFCI.getVecLength() ], dtype=ctypes.c_double)
79theFCI.FillRandom( theFCI.getVecLength() , GSvector )
80EnergyFCI = theFCI.GSDavidson(GSvector)
81
82# Clean-up
83del theFCI
84del Ham
85del Initializer
86
87# Check whether the test succeeded
88if (np.fabs(EnergyDMRG - EnergyFCI) < 1e-8):
89    print("================> Did test 4 succeed : yes")
90else:
91    print("================> Did test 4 succeed : no")
92
93