#!/usr/bin/env python3
"``naginterfaces.library.opt.handle_add_vars`` Python Example."
# NAG Copyright 2020.
from naginterfaces.base import utils
from naginterfaces.library import opt
[docs]def main():
"""
Example for :func:`naginterfaces.library.opt.handle_add_vars`
NAG Optimization Modelling suite:
adding variables to an optimization model.
This example program demonstrates how to edit an LP model using
the NAG Optimization Modeling Suite (NOMS) functionality.
>>> main()
naginterfaces.library.opt.handle_add_vars Python Example Results
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Solve the first LP
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E04MT, Interior point method for LP problems
Status: converged, an optimal solution found
Final primal objective value 8.500000E+02
Final dual objective value 8.500000E+02
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Primal variables:
idx Lower bound Value Upper bound
1 0.00000E+00 2.00000E+02 inf
2 0.00000E+00 1.00000E+02 1.00000E+02
<BLANKLINE>
The new variable has been added, solve the handle again
<BLANKLINE>
E04MT, Interior point method for LP problems
Status: converged, an optimal solution found
Final primal objective value 9.000000E+02
Final dual objective value 9.000000E+02
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Primal variables:
idx Lower bound Value Upper bound
1 0.00000E+00 5.00000E+01 inf
2 0.00000E+00 1.00000E+02 1.00000E+02
3 0.00000E+00 5.00000E+01 5.00000E+01
<BLANKLINE>
The new constraint has been added, solve the handle again
<BLANKLINE>
E04MT, Interior point method for LP problems
Status: converged, an optimal solution found
Final primal objective value 8.750000E+02
Final dual objective value 8.750000E+02
<BLANKLINE>
Primal variables:
idx Lower bound Value Upper bound
1 0.00000E+00 1.50000E+02 inf
2 0.00000E+00 5.00000E+01 1.00000E+02
3 0.00000E+00 5.00000E+01 5.00000E+01
"""
print(
'naginterfaces.library.opt.handle_add_vars Python Example Results'
)
print()
print('Solve the first LP')
print()
infbnd = 1.0e20
# Initialize the Optimization model handle with the number of variables
handle = opt.handle_init(2)
# Define a linear objective function
opt.handle_set_linobj(handle, cvec=[2.0, 4.5])
# Box constraints
opt.handle_set_simplebounds(
handle,
bl=[0.0, 0.0],
bu=[infbnd, 100])
# Set the linear constraints
opt.handle_set_linconstr(
handle,
bl=[-infbnd, -infbnd, -infbnd],
bu=[1500.0, 6000.0, 16000.0],
irowb=[1, 1, 2, 2, 3, 3],
icolb=[1, 2, 1, 2, 1, 2],
b=[1.2, 3.0, 6.0, 10.0, 40.0, 80.0]
)
# Set some alogorithmic options
for option in [
'Print Options = NO',
'Print Level = 1',
'Task = Max',
'Print Solution = X',
]:
opt.handle_opt_set(handle, option)
# Use an explicit I/O manager for abbreviated iteration output:
iom = utils.FileObjManager(locus_in_output=False)
# Solve the problem
opt.handle_solve_lp_ipm(handle, io_manager=iom)
# add a variable
opt.handle_add_vars(handle, nadd=1)
# Box Constraint on the new variable
opt.handle_set_bound(handle, comp='X', idx=3, bli=0.0, bui=50.0)
# Add the linear objective component
opt.handle_set_linobj_coeff(handle, idxci=3, ci=7.0)
# Add linear constraints coefficients
opt.handle_set_linconstr_coeff(handle, idlc=1, icolbj=3, bij=5.0)
opt.handle_set_linconstr_coeff(handle, idlc=2, icolbj=3, bij=12.0)
opt.handle_set_linconstr_coeff(handle, idlc=3, icolbj=3, bij=120.0)
print()
print('The new variable has been added, solve the handle again')
print()
# Solve the problem again
opt.handle_solve_lp_ipm(handle, io_manager=iom)
# Add a linear constraint
opt.handle_set_linconstr(
handle,
bl=[-infbnd],
bu=[100.0],
irowb=[1, 1],
icolb=[2, 3],
b=[1.0, 1.0]
)
print()
print('The new constraint has been added, solve the handle again')
print()
# Solve the problem again
opt.handle_solve_lp_ipm(handle, io_manager=iom)
# Destroy the handle:
opt.handle_free(handle)
if __name__ == '__main__':
import doctest
import sys
sys.exit(
doctest.testmod(
None, verbose=True, report=False,
optionflags=doctest.ELLIPSIS | doctest.REPORT_NDIFF,
).failed
)
