def obj(self, x):
"""
Return the value of the objective function at `x`. This function is
specialized since the original objective function only depends on a
subvector of `x`.
"""
return AmplModel.obj(self, x[:self.original_n])
def obj(self, x):
"""
Return the value of the objective function at `x`. This function is
specialized since the original objective function only depends on a
subvector of `x`.
"""
return AmplModel.obj(self, x[:self.original_n])
pi0 = nlp.pi0
n = nlp.n
m = nlp.m
print 'There are %d variables and %d constraints' % ( n, m )
max_n = min( n, 5 )
max_m = min( m, 5 )
print
print ' Printing at most 5 first components of vectors'
print
print 'Initial point: ', x0[:max_n]
print 'Lower bounds on x: ', nlp.Lvar[:max_n]
print 'Upper bounds on x: ', nlp.Uvar[:max_n]
print 'f( x0 ) = ', nlp.obj( x0 )
g0 = nlp.grad( x0 )
print 'grad f( x0 ) = ', g0[:max_n]
if max_m > 0:
print 'Initial multipliers: ', pi0[:max_m]
print 'Lower constraint bounds: ', nlp.Lcon[:max_m]
print 'Upper constraint bounds: ', nlp.Ucon[:max_m]
c0 = nlp.cons( x0 )
print 'c( x0 ) = ', c0[:max_m]
J = nlp.jac( x0 )
H = nlp.hess( x0, pi0 )
print
print ' nnzJ = ', J.nnz
print ' nnzH = ', H.nnz
pi0 = nlp.pi0
n = nlp.n
m = nlp.m
print 'There are %d variables and %d constraints' % (n, m)
max_n = min(n, 5)
max_m = min(m, 5)
print
print ' Printing at most 5 first components of vectors'
print
print 'Initial point: ', x0[:max_n]
print 'Lower bounds on x: ', nlp.Lvar[:max_n]
print 'Upper bounds on x: ', nlp.Uvar[:max_n]
print 'f( x0 ) = ', nlp.obj(x0)
g0 = nlp.grad(x0)
print 'grad f( x0 ) = ', g0[:max_n]
if max_m > 0:
print 'Initial multipliers: ', pi0[:max_m]
print 'Lower constraint bounds: ', nlp.Lcon[:max_m]
print 'Upper constraint bounds: ', nlp.Ucon[:max_m]
c0 = nlp.cons(x0)
print 'c( x0 ) = ', c0[:max_m]
J = nlp.jac(x0)
H = nlp.hess(x0, pi0)
print
print ' nnzJ = ', J.nnz
print ' nnzH = ', H.nnz
