linear programming in python?
Question:
I need to make a linear programming model. Here are the inequalities I’m using (for example):
6x + 4y <= 24
x + 2y <= 6
-x + y <= 1
y <= 2
I need to find the area described by these inequalities, and shade it in a graph, as well as keep track of the vertices of the bounding lines of this area, and draw the bounding line in a different color. See the graph below for an example of what I’m looking for.
.
I’m using Python 3.2, numpy, and matplotlib. Are there better modules for linear programming in Python?
Answers:
UPDATE: The answer has become somewhat outdated in the past 4 years,
here is an update. You have many options:
-
If you do not have to do it Python then it is a lot more easier to
do this in a modeling langage, see Any good tools to solve
integer programs on linux?
-
I personally use Gurobi these
days through its Python API. It is a commercial, closed-source
product but free for academic research.
-
With PuLP you can create MPS and LP files and then
solve them with GLPK, COIN CLP/CBC, CPLEX, or XPRESS through their
command-line interface. This approach has its advantages and
disadvantages.
-
The OR-Tools from Google is an open source software suite for optimization, tuned for tackling the world’s toughest problems in vehicle routing, flows, integer and linear programming, and constraint programming.
-
Pyomo is a Python-based, open-source optimization modeling language with a diverse set of optimization capabilities.
-
SciPy offers linear programming: scipy.optimize.linprog. (I have
never tried this one.)
-
Apparently, CVXOPT offers a Python interface to GLPK, I did
not know that. I have been using GLPK for 8 years now and I can
highly recommend GLPK. The examples and tutorial of CVXOPT seem really nice!
-
You can find other possibilites at in the Wikibook under
GLPK/Python. Note that many of these are not necessarily resticted
to GLPK.
The only time a graph is used to solve a linear program is for a homework problem. In all other cases, linear programming problems are solved through matrix linear algebra.
As for Python, while there are some pure-Python libraries, most people use a native library with Python bindings. There is a wide variety of free and commercial libraries for linear programming. For a detailed list, see Linear Programming in Wikipedia or the Linear Programming Software Survey in OR/MS Today.
Disclaimer: I currently work for Gurobi Optimization and formerly worked for ILOG, which provided CPLEX.
I would recommend using the PuLP python package. It has a nice interface and you can use differenty types of algorithms to solve LP.
lpsolve is the easiest to me. No need to install separate solver. It comes with in the package.
For solving the linear programming problem, you can use the scipy.optimize.linprog module in SciPy, which uses the Simplex algorithm.
The other answers have done a good job providing a list of solvers. However, only PuLP has been mentioned as a Python library to formulating LP models.
Another great option is Pyomo. Like PuLP, you can send the problem to any solver and read the solution back into Python. You can also manipulate solver parameters. A classmate and I compared the performance of PuLP and Pyomo back in 2015 and we found Pyomo could generate .LP files for the same problem several times more quickly than PuLP.
Here is a graphical representation of the problem, inspired from How to visualize feasible region for linear programming (with arbitrary inequalities) in Numpy/MatplotLib?
import numpy as np
import matplotlib.pyplot as plt
m = np.linspace(0,5,200)
x,y = np.meshgrid(m,m)
plt.imshow(((6*x+4*y<=24)&(x+2*y<=6)&(-x+y<=1)&(y<=2)&(x>=0)&(y>=0)).astype(int),
extent=(x.min(),x.max(),y.min(),y.max()),origin='lower',cmap='Greys',alpha=0.3);
# plot constraints
x = np.linspace(0, 5, 2000)
# 6*x+4*y<=24
y0 = 6-1.5*x
# x+2*y<=6
y1 = 3-0.5*x
# -x+y<=1
y2 = 1+x
# y <= 2
y3 = (x*0) + 2
# x >= 0
y4 = x*0
plt.plot(x, y0, label=r'$6x+4yleq24$')
plt.plot(x, y1, label=r'$x+2yleq6$')
plt.plot(x, y2, label=r'$-x+yleq1$')
plt.plot(x, 2*np.ones_like(x), label=r'$yleq2$')
plt.plot(x, y4, label=r'$xgeq0$')
plt.plot([0,0],[0,3], label=r'$ygeq0$')
xv = [0,0,1,2,3,4,0]; yv = [0,1,2,2,1.5,0,0]
plt.plot(xv,yv,'ko--',markersize=7,linewidth=2)
for i in range(len(xv)):
plt.text(xv[i]+0.1,yv[i]+0.1,f'({xv[i]},{yv[i]})')
plt.xlim(0,5); plt.ylim(0,3); plt.grid(); plt.tight_layout()
plt.legend(loc=1); plt.xlabel('x'); plt.ylabel('y')
plt.show()
The problem is missing an objective function so any of the shaded points satisfy the inequalities. If it did have an objective function (e.g. Maximize x+y) then many capable Python solvers can handle this problem. Here is a Linear Programming example in GEKKO that also supports mixed integer, nonlinear, and differential constraints.
from gekko import GEKKO
m = GEKKO(remote=False)
x,y = m.Array(m.Var,2,lb=0)
m.Equations([6*x+4*y<=24,x+2*y<=6,-x+y<=1,y<=2])
m.Maximize(x+y)
m.solve(disp=False)
Large scale LP problems are solved in matrix form or in sparse matrix form where only the non-zeros of the matrices are stored. There is a tutorial on LP solutions with a few examples that I developed for a university course.
I need to make a linear programming model. Here are the inequalities I’m using (for example):
6x + 4y <= 24
x + 2y <= 6
-x + y <= 1
y <= 2
I need to find the area described by these inequalities, and shade it in a graph, as well as keep track of the vertices of the bounding lines of this area, and draw the bounding line in a different color. See the graph below for an example of what I’m looking for.
.
I’m using Python 3.2, numpy, and matplotlib. Are there better modules for linear programming in Python?
UPDATE: The answer has become somewhat outdated in the past 4 years,
here is an update. You have many options:
-
If you do not have to do it Python then it is a lot more easier to
do this in a modeling langage, see Any good tools to solve
integer programs on linux? -
I personally use Gurobi these
days through its Python API. It is a commercial, closed-source
product but free for academic research. -
With PuLP you can create MPS and LP files and then
solve them with GLPK, COIN CLP/CBC, CPLEX, or XPRESS through their
command-line interface. This approach has its advantages and
disadvantages. -
The OR-Tools from Google is an open source software suite for optimization, tuned for tackling the world’s toughest problems in vehicle routing, flows, integer and linear programming, and constraint programming.
-
Pyomo is a Python-based, open-source optimization modeling language with a diverse set of optimization capabilities.
-
SciPy offers linear programming: scipy.optimize.linprog. (I have
never tried this one.) -
Apparently, CVXOPT offers a Python interface to GLPK, I did
not know that. I have been using GLPK for 8 years now and I can
highly recommend GLPK. The examples and tutorial of CVXOPT seem really nice! -
You can find other possibilites at in the Wikibook under
GLPK/Python. Note that many of these are not necessarily resticted
to GLPK.
The only time a graph is used to solve a linear program is for a homework problem. In all other cases, linear programming problems are solved through matrix linear algebra.
As for Python, while there are some pure-Python libraries, most people use a native library with Python bindings. There is a wide variety of free and commercial libraries for linear programming. For a detailed list, see Linear Programming in Wikipedia or the Linear Programming Software Survey in OR/MS Today.
Disclaimer: I currently work for Gurobi Optimization and formerly worked for ILOG, which provided CPLEX.
I would recommend using the PuLP python package. It has a nice interface and you can use differenty types of algorithms to solve LP.
lpsolve is the easiest to me. No need to install separate solver. It comes with in the package.
For solving the linear programming problem, you can use the scipy.optimize.linprog module in SciPy, which uses the Simplex algorithm.
The other answers have done a good job providing a list of solvers. However, only PuLP has been mentioned as a Python library to formulating LP models.
Another great option is Pyomo. Like PuLP, you can send the problem to any solver and read the solution back into Python. You can also manipulate solver parameters. A classmate and I compared the performance of PuLP and Pyomo back in 2015 and we found Pyomo could generate .LP files for the same problem several times more quickly than PuLP.
Here is a graphical representation of the problem, inspired from How to visualize feasible region for linear programming (with arbitrary inequalities) in Numpy/MatplotLib?
import numpy as np
import matplotlib.pyplot as plt
m = np.linspace(0,5,200)
x,y = np.meshgrid(m,m)
plt.imshow(((6*x+4*y<=24)&(x+2*y<=6)&(-x+y<=1)&(y<=2)&(x>=0)&(y>=0)).astype(int),
extent=(x.min(),x.max(),y.min(),y.max()),origin='lower',cmap='Greys',alpha=0.3);
# plot constraints
x = np.linspace(0, 5, 2000)
# 6*x+4*y<=24
y0 = 6-1.5*x
# x+2*y<=6
y1 = 3-0.5*x
# -x+y<=1
y2 = 1+x
# y <= 2
y3 = (x*0) + 2
# x >= 0
y4 = x*0
plt.plot(x, y0, label=r'$6x+4yleq24$')
plt.plot(x, y1, label=r'$x+2yleq6$')
plt.plot(x, y2, label=r'$-x+yleq1$')
plt.plot(x, 2*np.ones_like(x), label=r'$yleq2$')
plt.plot(x, y4, label=r'$xgeq0$')
plt.plot([0,0],[0,3], label=r'$ygeq0$')
xv = [0,0,1,2,3,4,0]; yv = [0,1,2,2,1.5,0,0]
plt.plot(xv,yv,'ko--',markersize=7,linewidth=2)
for i in range(len(xv)):
plt.text(xv[i]+0.1,yv[i]+0.1,f'({xv[i]},{yv[i]})')
plt.xlim(0,5); plt.ylim(0,3); plt.grid(); plt.tight_layout()
plt.legend(loc=1); plt.xlabel('x'); plt.ylabel('y')
plt.show()
The problem is missing an objective function so any of the shaded points satisfy the inequalities. If it did have an objective function (e.g. Maximize x+y) then many capable Python solvers can handle this problem. Here is a Linear Programming example in GEKKO that also supports mixed integer, nonlinear, and differential constraints.
from gekko import GEKKO
m = GEKKO(remote=False)
x,y = m.Array(m.Var,2,lb=0)
m.Equations([6*x+4*y<=24,x+2*y<=6,-x+y<=1,y<=2])
m.Maximize(x+y)
m.solve(disp=False)
Large scale LP problems are solved in matrix form or in sparse matrix form where only the non-zeros of the matrices are stored. There is a tutorial on LP solutions with a few examples that I developed for a university course.