I'm trying to optimize a configuration X (boolean), such that the total price : base_price + discount, on a configuration is minimized, but the problem formulation gives a Matmul error since x is a cvxpy Variable and thus doesn't conform to the Numpy shape even though it was defined with the correct length.
n = len(Configuration)
x = cp.Variable(n, boolean=True)
problem = cp.Problem(cp.Minimize(base_price + price@(price_rules_A@x <= price_rules_B)), [
config_rules_A@x <= config_rules_B,
config_rules_2A@x == config_rules_2B
])
# where price@(price_rules_A@x <= price_rules_B) is the total discount
# and price, price_rules_A and price_rules_B are numpy arrays
The error i get is
ValueError: matmul: Input operand 1 does not have enough dimensions (has 0, gufunc core with signature (n?,k),(k,m?)->(n?,m?) requires 1)
I expect it to find an optimal config for x ( 0010110...) such that the discount is minimized but it doesn't. Any idea what might be causing this?
Assuming the evaluation of the inequality in the objective function is suppose to work as index to price, you can rewrite the function as
cp.Minimize(base_price + price@(1-(price_rules_B - price_rules_A@x))
Then the elements in price where the inequality is true will be summed.