Linear optimisation in python using cvxpy

Question

this is a simple example of LP using cvxpy

import cvxpy as cp
x1 = cp.Variable()
x2 = cp.Variable()
objective = cp.Minimize(0.3*x1 + 0.4*x2)
constraints = [
    4.6*x1 + 58.8*x2 >= 300,
    52*x1 + 33*x2 >= 2000,
    152*x1 + 133*x2 >= 5000,
]
linear_program = cp.Problem(objective, constraints)
result = linear_program.solve()

my question is if I have the same constraints in the form of a dataFrame

A = pd.DataFrame([[4.6,58.8],[52,33],[152,133]])
c= [0.3,0.4]
b = [300,2000,5000]

then how do I transform A,b and c to put it through the cvxpy optimiser? i.e. converting the dataFrame A, b to the format

4.6*x1 + 58.8*x2 >= 300,
52*x1 + 33*x2 >= 2000,
152*x1 + 133*x2 >= 5000,

and c to

0.3*x1 + 0.4*x2

Its easy to manually type if the number of constraints and variable are limited (3 and 2 in this case), but my acutal problem has over 50+ variables and over 80+ constraints in the form of dataFrames (and the numbers can keep changing so I cant hardcode the constraints).

#########Edits- updated the shape of the dataFrame A, which was previously incorrect

Riley · Accepted Answer · 2022-05-28 00:31:04Z

1

Firstly, change your lists to numpy arrays. Then create an array of x variables, instead of one at a time then use it with matrix multiplication to add constraint. Eg

A @ x >= b

See example here https://www.cvxpy.org/examples/basic/linear_program.html

answered May 28, 2022 at 0:31

Riley

2,2801 gold badge8 silver badges18 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Linear optimisation in python using cvxpy

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related