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
print
print ' Printing at most first 5x5 principal submatrix'
print
print 'J( x0 ) = ', J[:max_m,:max_n]
print 'Hessian (lower triangle):', H[:max_n,:max_n]
def cons(self, x):
"""
Evaluate the vector of general constraints for the modified problem.
Constraints are stored in the order in which they appear in the
original problem. If constraint i is a range constraint, c[i] will
be the constraint that has the slack on the lower bound on c[i].
The constraint with the slack on the upper bound on c[i] will be stored
in position m + k, where k is the position of index i in
rangeC, i.e., k=0 iff constraint i is the range constraint that
appears first, k=1 iff it appears second, etc.
Constraints appear in the following order:
1. [ c ] general constraints in origninal order
2. [ cR ] 'upper' side of range constraints
3. [ b ] linear constraints corresponding to bounds on original problem
4. [ bR ] linear constraints corresponding to 'upper' side of two-sided
bounds
"""
n = self.n
on = self.original_n
m = self.m
om = self.original_m
equalC = self.equalC
lowerC = self.lowerC
nlowerC = self.nlowerC
upperC = self.upperC
nupperC = self.nupperC
rangeC = self.rangeC
nrangeC = self.nrangeC
mslow = on + self.n_con_low
msup = mslow + self.n_con_up
s_low = x[on:mslow] # len(s_low) = n_con_low
s_up = x[mslow:msup] # len(s_up) = n_con_up
c = numpy.empty(m)
c[:om] = AmplModel.cons(self, x[:on])
c[om:om + nrangeC] = c[rangeC]
c[equalC] -= self.Lcon[equalC]
c[lowerC] -= self.Lcon[lowerC]
c[lowerC] -= s_low[:nlowerC]
c[upperC] -= self.Ucon[upperC]
c[upperC] *= -1
c[upperC] -= s_up[:nupperC]
c[rangeC] -= self.Lcon[rangeC]
c[rangeC] -= s_low[nlowerC:]
c[om:om + nrangeC] -= self.Ucon[rangeC]
c[om:om + nrangeC] *= -1
c[om:om + nrangeC] -= s_up[nupperC:]
# Add linear constraints corresponding to bounds on original problem
lowerB = self.lowerB
nlowerB = self.nlowerB
Lvar = self.Lvar
upperB = self.upperB
nupperB = self.nupperB
Uvar = self.Uvar
rangeB = self.rangeB
nrangeB = self.nrangeB
nt = on + self.n_con_low + self.n_con_up
ntlow = nt + self.n_var_low
t_low = x[nt:ntlow]
t_up = x[ntlow:]
b = c[om + nrangeC:]
b[:nlowerB] = x[lowerB] - Lvar[lowerB] - t_low[:nlowerB]
b[nlowerB:nlowerB +
nrangeB] = x[rangeB] - Lvar[rangeB] - t_low[nlowerB:]
b[nlowerB + nrangeB:nlowerB + nrangeB +
nupperB] = Uvar[upperB] - x[upperB] - t_up[:nupperB]
b[nlowerB + nrangeB +
nupperB:] = Uvar[rangeB] - x[rangeB] - t_up[nupperB:]
return c
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
print
print ' Printing at most first 5x5 principal submatrix'
print
print 'J( x0 ) = ', J[:max_m, :max_n]
print 'Hessian (lower triangle):', H[:max_n, :max_n]
def cons(self, x):
"""
Evaluate the vector of general constraints for the modified problem.
Constraints are stored in the order in which they appear in the
original problem. If constraint i is a range constraint, c[i] will
be the constraint that has the slack on the lower bound on c[i].
The constraint with the slack on the upper bound on c[i] will be stored
in position m + k, where k is the position of index i in
rangeC, i.e., k=0 iff constraint i is the range constraint that
appears first, k=1 iff it appears second, etc.
Constraints appear in the following order:
1. [ c ] general constraints in origninal order
2. [ cR ] 'upper' side of range constraints
3. [ b ] linear constraints corresponding to bounds on original problem
4. [ bR ] linear constraints corresponding to 'upper' side of two-sided
bounds
"""
n = self.n ; on = self.original_n
m = self.m ; om = self.original_m
equalC = self.equalC
lowerC = self.lowerC ; nlowerC = self.nlowerC
upperC = self.upperC ; nupperC = self.nupperC
rangeC = self.rangeC ; nrangeC = self.nrangeC
mslow = on + self.n_con_low
msup = mslow + self.n_con_up
s_low = x[on:mslow] # len(s_low) = n_con_low
s_up = x[mslow:msup] # len(s_up) = n_con_up
c = numpy.empty(m)
c[:om] = AmplModel.cons(self, x[:on])
c[om:om+nrangeC] = c[rangeC]
c[equalC] -= self.Lcon[equalC]
c[lowerC] -= self.Lcon[lowerC] ; c[lowerC] -= s_low[:nlowerC]
c[upperC] -= self.Ucon[upperC] ; c[upperC] *= -1
c[upperC] -= s_up[:nupperC]
c[rangeC] -= self.Lcon[rangeC] ; c[rangeC] -= s_low[nlowerC:]
c[om:om+nrangeC] -= self.Ucon[rangeC]
c[om:om+nrangeC] *= -1
c[om:om+nrangeC] -= s_up[nupperC:]
# Add linear constraints corresponding to bounds on original problem
lowerB = self.lowerB ; nlowerB = self.nlowerB ; Lvar = self.Lvar
upperB = self.upperB ; nupperB = self.nupperB ; Uvar = self.Uvar
rangeB = self.rangeB ; nrangeB = self.nrangeB
nt = on + self.n_con_low + self.n_con_up
ntlow = nt + self.n_var_low
t_low = x[nt:ntlow]
t_up = x[ntlow:]
b = c[om+nrangeC:]
b[:nlowerB] = x[lowerB] - Lvar[lowerB] - t_low[:nlowerB]
b[nlowerB:nlowerB+nrangeB] = x[rangeB] - Lvar[rangeB] - t_low[nlowerB:]
b[nlowerB+nrangeB:nlowerB+nrangeB+nupperB] = Uvar[upperB] - x[upperB] - t_up[:nupperB]
b[nlowerB+nrangeB+nupperB:] = Uvar[rangeB] - x[rangeB] - t_up[nupperB:]
return c
