def test_load():
print '\n[test_load]'
shape = (nr,nc)
# simple peps
vec = np.load('data/peps_vec3by3.npy')
peps0 = peps.aspeps(vec, (nr,nc), pdim, bond)
ovlp,php = nAverage_fpeps3by3(peps0)
print 'nAveraged=',ovlp,php,php/ovlp
ovlp,etot = energy_fpeps3by3(peps0,0.0)
print 'ovlp,etot=',ovlp,etot,etot/ovlp
ne = 4.0
npts = 5
xts = np.array([(2.0*i-1.0)/(2*npts)*np.pi for i in range(1,npts+1)])
wts = np.array([np.pi/npts]*npts)*[np.exp(-1.j*x*ne) for x in xts]
print '\nQuadrature[Chebyshev-Gauss]'
print 'xts=',xts
print 'wts=',wts
def energy_fn2(vec, pdim, bond):
P = peps.aspeps(vec, (nr,nc), pdim, bond)
PP_tot = 0.
PHP_tot = 0.
for ipt in range(npts):
PP,PHP = energy_fpeps3by3(P,xts[ipt])
PP_tot += PP*wts[ipt]
PHP_tot += PHP*wts[ipt]
#print ' PHP,PP',PHP,PP
e = PHP_tot/PP_tot
print ' PHP_tot,PP_tot=',PHP_tot,PP_tot
print ' e_tot=',e
return e
def energy_fn(vec, pdim, bond):
P = peps.aspeps(vec, (nr,nc), pdim, bond)
PP,PHP = energy_fpeps3by3(P)
e = PHP/PP
print ' PHP,PP,PHP/PP,eav=',PHP,PP,e,e/(nr*nc)
return PHP/PP
def bound_energy_fn(vec):
return energy_fn2(vec, pdim, bond)
vec = peps.flatten(peps0)
import scipy.optimize
import autograd
deriv = autograd.grad(bound_energy_fn)
print 'nparams=',len(vec)
def save_vec(vec):
fname = 'peps_vec1_3by3'
np.save(fname,vec)
print ' --- save vec into fname=',fname
return 0
# Optimize
result = scipy.optimize.minimize(bound_energy_fn, jac=deriv, x0=vec,\
tol=1.e-3, callback=save_vec)
P0 = peps.aspeps(result.x, (nr,nc), pdim, bond)
print "final =",bound_energy_fn(peps.flatten(P0))
return 0
def test_plot():
elst = np.load('data/energy.npy')
import matplotlib.pyplot as plt
efci = -3.700331728271
plt.plot(np.log10(elst - efci), 'ro-')
plt.savefig('data/convergence.pdf')
plt.show()
return 0
def test_opt():
print '\n[test_opt]'
shape = (nr, nc)
# simple peps
vec = np.load('peps_vec1_3by3.npy')
vec = vec / 5
peps0 = peps.aspeps(vec, (nr, nc), pdim, bond)
bondL = bond + 1
pepsc = peps.random(peps0.shape, pdim, 1, fac=1.e-2)
peps0 = peps.add(peps0, pepsc)
ovlp, php = nAverage_fpeps3by3(peps0)
print 'nAveraged=', ovlp, php, php / ovlp
ovlp, etot = energy_fpeps3by3(peps0)
print 'ovlp,etot=', ovlp, etot, etot / ovlp
def energy_fn(vec, pdim, bondL):
P = peps.aspeps(vec, (nr, nc), pdim, bondL)
PP, PHP = energy_fpeps3by3(P)
e = PHP / PP
print ' PHP,PP,PHP/PP,eav=', PHP, PP, e, e / (nr * nc)
return PHP / PP
def bound_energy_fn(vec):
return energy_fn(vec, pdim, bondL)
vec = peps.flatten(peps0)
import scipy.optimize
import autograd
deriv = autograd.grad(bound_energy_fn)
print 'nparams=', len(vec)
def save_vec(vec):
fname = 'peps_vec2_3by3'
np.save(fname, vec)
print ' --- save vec into fname=', fname
return 0
# Optimize
result = scipy.optimize.minimize(bound_energy_fn, jac=deriv, x0=vec,\
tol=1.e-4, callback=save_vec)
P0 = peps.aspeps(result.x, (nr, nc), pdim, bondL)
print "final =", energy_fn(peps.flatten(P0), pdim, bondL)
return 0
def test_load():
print '[test_load]'
shape = (nr, nc)
# simple peps
vec = np.load('data/peps_vec2by2.npy')
peps0 = peps.aspeps(vec, (nr, nc), pdim, bond)
ovlp, etot = energy_fpeps2by2(peps0)
ovlp, php = nAverage_fpeps2by2(peps0)
print 'etot=', ovlp, etot, etot / ovlp
print 'nAverage=', ovlp, php, php / ovlp
exit()
def energy_fn(vec, pdim, bond):
P = peps.aspeps(vec, (nr, nc), pdim, bond)
PP, PHP = energy_fpeps2by2(P)
e = PHP / PP
print ' PHP,PP,PHP/PP,eav=', PHP, PP, e, e / (nr * nc)
return PHP / PP
def bound_energy_fn(vec):
return energy_fn(vec, pdim, bond)
vec = peps.flatten(peps0)
import scipy.optimize
import autograd
deriv = autograd.grad(bound_energy_fn)
print 'nparams=', len(vec)
elst = []
def save_vec(vec):
fname = 'data/peps_vec1'
np.save(fname, vec)
e = bound_energy_fn(vec)
elst.append(e)
print ' --- save vec into fname=', fname, ' e=', e
return 0
# Optimize
result = scipy.optimize.minimize(bound_energy_fn, jac=deriv, x0=vec,\
tol=1.e-4, callback=save_vec)
P0 = peps.aspeps(result.x, (nr, nc), pdim, bond)
print "final =", energy_fn(peps.flatten(P0), pdim, bond)
np.save('data/energy', elst)
return 0