#!/usr/bin/env python3
"``naginterfaces.library.opt.nlp1_solve`` Python Example."
# NAG Copyright 2017-2019.
# pylint: disable=invalid-name,too-many-locals
import numpy as np
from naginterfaces.library import opt
[docs]def main():
"""
Example for :func:`naginterfaces.library.opt.nlp1_solve`.
Dense NLP.
Demonstrates handling optional algorithmic parameters.
>>> main()
naginterfaces.library.opt.nlp1_solve Python Example Results.
Solve Hock and Schittkowski Problem 71.
Final objective value is 1.7014017e+01
"""
print(
'naginterfaces.library.opt.nlp1_solve Python Example Results.'
)
print('Solve Hock and Schittkowski Problem 71.')
def cb_confun(mode, needc, x, cjac, _nstate):
"""The nonlinear constraints."""
c = np.zeros(len(needc))
if needc[0] > 0:
if mode in [0, 2]:
c[0] = (x[0]**2 + x[1]**2 + x[2]**2 + x[3]**2)
if mode == 2:
cjac[0, :] = 2*x
if needc[1] > 0:
if mode in [0, 2]:
c[1] = x[0]*x[1]*x[2]*x[3]
if mode == 2:
cjac[1, :] = [
x[1]*x[2]*x[3],
x[0]*x[2]*x[3],
x[0]*x[1]*x[3],
x[0]*x[1]*x[2],
]
return c, cjac
def cb_objfun(mode, x, objgrd, _nstate):
"""The objective function."""
if mode in [0, 2]:
objf = x[0]*x[3]*(x[0] + x[1] + x[2]) + x[2]
else:
objf = 0.
if mode == 2:
objgrd[:] = [
x[3]*(2*x[0] + x[1] + x[2]),
x[0]*x[3],
x[0]*x[3] + 1.0,
x[0]*(x[0] + x[1] + x[2]),
]
return objf, objgrd
# Initialize the solver:
comm = opt.nlp1_init('nlp1_solve')
# The initial guess:
x = [1., 5., 5., 1.]
# The linear constraints:
a = np.array([[1.]*len(x)])
# The bounds:
bl = [1., 1., 1., 1., -1.0E+25, -1.0E+25, 25.]
bu = [5., 5., 5., 5., 20., 40., 1.0E+25]
# To set algorithmic options:
opt.nlp1_option_string('Infinite Bound Size = 1.0e20', comm)
objf = opt.nlp1_solve(
a, bl, bu, cb_objfun, x, comm,
confun=cb_confun,
).objf
print('Final objective value is {:.7e}'.format(objf))
if __name__ == '__main__':
import doctest
import sys
sys.exit(
doctest.testmod(
None, verbose=True, report=False,
optionflags=doctest.REPORT_NDIFF,
).failed
)
