# Check second partial derivatives in turn.
for i in range(n):
xph = self.x.copy()
xph[i] += h
xmh = self.x.copy()
xmh[i] -= h
dgdx = (nlp.igrad(k, xph) - nlp.igrad(k, xmh)) / (2 * h)
for j in range(i + 1):
dgjdxi = dgdx[j]
err = abs(Hk[i, j] - dgjdxi) / (1 + abs(Hk[i, j]))
line = self.d2fmt % (k + 1, i, j, Hk[i, j], dgjdxi, err)
if verbose:
sys.stderr.write(line)
if err > self.tol:
errs.append(line)
return errs
if __name__ == '__main__':
import sys
from nlpy.model import AmplModel
nlp = AmplModel(sys.argv[1])
print 'Checking at x = ', nlp.x0
derchk = DerivativeChecker(nlp, nlp.x0)
derchk.check(verbose=True)
nlp.close()
if err > 1.0e-12:
err_matvec += 1
print 'Inaccurate matvec:'
print hp1
print hp2
print 'Found %d inaccurate matvecs.' % err_matvec
# Solve problem
# TRIP.SolveOuter()
# Display final statistics
# print 'Final variables:'; print TRIP.x
# print 'Final multipliers:'; print TRIP.z
# print
# print 'Optimal: ', TRIP.optimal
# print 'Variables: ', TRIP.nlp.n
# print '# lower, upper, 2-sided bounds: %-d, %-d, %-d' % \
# (TRIP.nlowerB, TRIP.nupperB, TRIP.nrangeB)
# print 'Primal feasibility error = %15.7e' % TRIP.pRes
# print 'Dual feasibility error = %15.7e' % TRIP.dRes
# print 'Complementarity error = %15.7e' % TRIP.cRes
# print 'Number of function evals = %d' % TRIP.nlp.feval
# print 'Number of gradient evals = %d' % TRIP.nlp.geval
# print 'Number of Hessian evals = %d' % TRIP.nlp.Heval
# print 'Number of matvec products = %d' % TRIP.nlp.Hprod
# print 'Final objective value = %15.7e' % TRIP.f
# print 'Solution time: ', TRIP.tsolve
nlp.close()
# Check for equality-constrained problem.
n_ineq = nlp.nlowerC + nlp.nupperC + nlp.nrangeC
if nlp.nbounds > 0 or n_ineq > 0:
msg = '%s has %d bounds and %d inequality constraints\n'
log.error(msg % (nlp.name, nlp.nbounds, n_ineq))
error = True
else:
ProblemName = os.path.basename(ProblemName)
if ProblemName[-3:] == '.nl':
ProblemName = ProblemName[:-3]
t_setup, funn = pass_to_funnel(nlp, **opts)
if multiple_problems:
log.info(fmt % (ProblemName, funn.niter, funn.nlp.feval, funn.f,
funn.dResid, funn.pResid,
t_setup, funn.tsolve, funn.optimal))
nlp.close() # Close model.
if not multiple_problems and not error:
# Output final statistics
log.info('--------------------------------')
log.info('Funnel: End of Execution')
log.info(' Problem : %-s' % ProblemName)
log.info(' Number of variables : %-d' % nlp.n)
log.info(' Number of linear constraints : %-d' % nlp.nlin)
log.info(' Number of general constraints: %-d' % (nlp.m - nlp.nlin))
log.info(' Initial/Final Objective : %-g/%-g' % (funn.f0, funn.f))
log.info(' Number of iterations : %-d' % funn.niter)
log.info(' Number of function evals : %-d' % funn.nlp.feval)
log.info(' Number of gradient evals : %-d' % funn.nlp.geval)
#log.info(' Number of Hessian evals : %-d' % funn.nlp.Heval)
log.info(' Number of Hessian matvecs : %-d' % funn.nlp.Hprod)
