I'm using CVXPY in Python 3 to try to model the following linear program in X
(N by T matrix). Let
R
be an N by 1 matrix where each row is the sum of the entire row of values inX
.P
be a 1 by N matrix defined in terms ofX
such asP_t = 1/(G-d-x_t)
.
I want to solve for an ideal such that:
minimize (X x P)
subject to:
The sum of reach row i in X has to be at least the value in R_i
Each value in X has to be at least 0
Any thoughts? I have the following code and not getting any luck:
from cvxpy import *
X = Variable(N,T)
P = np.random.randn(T, 1)
R = cumsum(X,axis=0) # using cumsum because
http://www.cvxpy.org/en/latest/tutorial/functions/index.html#vector-matrix-functions
objective = Minimize(sum_entries(square(X*P))) #think this is good
constraints = [0 <= X, cumsum(X,axis=0) >= R]
prob = Problem(objective, constraints)