`mip-tool` is a package for Python-MIP.
Project description
MIP-Tool
MIP-Tool is a package for Python-MIP.
Installation
pip install pandas
pip install mip-tool
Example
Non-convex piecewise linear constraint
Maximize y which is on points of (-2, 6), (-1, 7), (2, -2), (4, 5).
import numpy as np
from mip import INF, Model, OptimizationStatus
from mip_tool import add_lines, show_model
m = Model()
x = m.add_var("x", lb=-INF)
y = m.add_var("y", obj=-1)
curve = np.array([[-2, 6], [-1, 7], [2, -2], [4, 5]])
add_lines(m, curve, x, y)
m.verbose = 0
m.optimize()
assert m.status == OptimizationStatus.OPTIMAL
assert (x.x, y.x) == (-1, 7)
show_model(m)
Output
\Problem name:
Minimize
OBJROW: - y
Subject To
constr(0): - x + w_0 + w_1 + w_2 = 2
constr(1): - y + w_0 -3 w_1 + 3.50000 w_2 = -6
constr(2): - w_0 + z_0 <= -0
constr(3): w_0 <= 1
constr(4): - w_1 + 3 z_1 <= -0
constr(5): - w_1 + 3 z_0 >= -0
constr(6): - w_2 + 2 z_1 >= -0
Bounds
x Free
0 <= z_0 <= 1
0 <= z_1 <= 1
Integers
z_0 z_1
End
F example
Easy to understand using F.
attention: Change Model and Var when using mip_tool.func.
from mip_tool.func import F
m = Model()
x = m.add_var("x")
y = m.add_var("y", obj=-1)
m += y <= F([[0, 2], [1, 3], [2, 2]], x)
m.verbose = 0
m.optimize()
print(x.x, y.x) # 1.0 3.0
y <= F(curve, x)
andy >= F(curve, x)
calladd_lines_conv
.y == F(curve, x)
callsadd_lines
.
pandas.DataFrame example
attention: Change Seies when using mip_tool.func.
import pandas as pd
from mip import Model, maximize, xsum
from mip_tool.func import addvars
A = pd.DataFrame([[1, 2], [3, 1]])
b = pd.Series([16, 18])
m = Model()
x = addvars(m, A, "")
m.objective = maximize(xsum(x))
m += A @ x <= b
m.verbose = 0
m.optimize()
print(x.astype(float)) # [4. 6.]
Expression m += A.T.apply(lambda row: xsum(row * x)) <= b
may be faster than m += A @ x <= b
.
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mip-tool-0.2.0.tar.gz
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