I have a problem related to portfolio opimisation, I am trying to maximise an objective which is: say I have column A, and solution weights, my objective is to minimise the sum of [(weights - column A)^ 2 / column A], in other words, it's a problem of squared difference divided by column A, and I would like to minimise the sum of those. import cvxpy as cp # size 50, as there are 50 values in column A, and I would like to have 50 individual weights wts = cp.Variable(50) # Some constraints, i.e. total weights = 100%, and the sum of weights of index positions 1, 2, 3, 4 to be less than 0.50. constraints = [cp.sum(wts) == 1.0] constraints = constraints.append([cp.sum(wts[[1,2,3,4]]) <0.50]) # Objective sum_square = cp.sum(cp.square(wts-index_data['A'].values) / index_data['A'].values) prob = cp.Problem(cp.Minimize(sum_square), constraints) prob.solve() which unfortunately did not work for me, is there some function that I'm unaware of? or is there any problem with the set up of my problem? as I get cvxpy.error.DCPError: Problem does not follow DCP rules. Specifically: The objective is not DCP. Continue reading...
