f = [1, 2, 3, 4, 5, 4, 2, 1]
# indexing starts from ZERO!
# while in native lpsolve-python wrapper from 1
# so if you used [5,8] for native lp_solve python binding
# you should use [4,7] instead
intVars = [4, 7]
lb = -1.5 * ones(8)
ub = 15 * ones(8)
A = zeros((5, 8))
b = zeros(5)
for i in xrange(5):
for j in xrange(8):
A[i,j] = -8+sin(8*i) + cos(15*j)
b[i] = -150 + 80*sin(80*i)
p = MILP(f=f, lb=lb, ub=ub, A=A, b=b, intVars=intVars)
# if file name not ends with '.MPS' or '.mps'
# then '.mps' will be appended
success = p.exportToMPS('/home/dmitrey/PyTest/milp_1')
# or write into current dir:
# success = p.exportToMPS('milp')
# success is False if a error occurred (read-only file system, no write access, etc)
# elseware success is True
# f_opt is 25.801450769161505
# x_opt is [ 15. 10.15072538 -1.5 -1.5 -1. -1.5 -1.5 15.]
# Define some constraints
cons = [x+5*y<15, x[0]<-5, f1<[25, 35], f1>-100, 2*f1+4*z<[80, 800], 5*f2+4*z<100, [-5.5, -4.5]<x, x<1, -17<y, y<20, -4000<z, z<4]
# Create prob
# old-style:
#p = MILP(obj, startPoint, intVars = [y, z], constraints=cons)
# new (OpenOpt v 0.37+):
p = MILP(obj, startPoint, constraints=cons)
# Solve
r = p.minimize('lpSolve', iprint=-1) # glpk is name of the solver involved, see OOF doc for more arguments
# Decode solution
s = r.xf
print('Solution: x = %s y = %f z = %f' % (str(s[x]), s[y], s[z]))
# Solution: x = [-5.25 -4.5 ] y = 3.000000 z = -33.000000
# OPTIONAL: you can export the problem into MPS format file
# (lpsolve and its Python binding should be properly installed,
# you may take a look at the instructions from openopt.org/LP)
# if file name not ends with '.MPS' or '.mps'
# then '.mps' will be appended
success = p.exportToMPS('milp_1')
# success is False if a error occurred (read-only file system, no write access, etc)
# elseware success is True
# You can solve problems defined in MPS files
# with a variety of solvers at NEOS server for free
# http://neos.mcs.anl.gov/
# BTW they have Python API along with web API and other
]
# Create prob
# old-style:
#p = MILP(obj, startPoint, intVars = [y, z], constraints=cons)
# new (OpenOpt v 0.37+):
p = MILP(obj, startPoint, constraints=cons)
# Solve
r = p.minimize(
'lpSolve', iprint=-1
) # glpk is name of the solver involved, see OOF doc for more arguments
# Decode solution
s = r.xf
print('Solution: x = %s y = %f z = %f' % (str(s[x]), s[y], s[z]))
# Solution: x = [-5.25 -4.5 ] y = 3.000000 z = -33.000000
# OPTIONAL: you can export the problem into MPS format file
# (lpsolve and its Python binding should be properly installed,
# you may take a look at the instructions from openopt.org/LP)
# if file name not ends with '.MPS' or '.mps'
# then '.mps' will be appended
success = p.exportToMPS('milp_1')
# success is False if a error occurred (read-only file system, no write access, etc)
# elseware success is True
# You can solve problems defined in MPS files
# with a variety of solvers at NEOS server for free
# http://neos.mcs.anl.gov/
# BTW they have Python API along with web API and other
f = [1, 2, 3, 4, 5, 4, 2, 1]
# indexing starts from ZERO!
# while in native lpsolve-python wrapper from 1
# so if you used [5,8] for native lp_solve python binding
# you should use [4,7] instead
intVars = [4, 7]
lb = -1.5 * ones(8)
ub = 15 * ones(8)
A = zeros((5, 8))
b = zeros(5)
for i in xrange(5):
for j in xrange(8):
A[i, j] = -8 + sin(8 * i) + cos(15 * j)
b[i] = -150 + 80 * sin(80 * i)
p = MILP(f=f, lb=lb, ub=ub, A=A, b=b, intVars=intVars)
# if file name not ends with '.MPS' or '.mps'
# then '.mps' will be appended
success = p.exportToMPS('/home/dmitrey/PyTest/milp_1')
# or write into current dir:
# success = p.exportToMPS('milp')
# success is False if a error occurred (read-only file system, no write access, etc)
# elseware success is True
# f_opt is 25.801450769161505
# x_opt is [ 15. 10.15072538 -1.5 -1.5 -1. -1.5 -1.5 15.]
