python - CVX to cvxpy and cvxopt -

i've been trying pass code matlab python. have same convex optimization problem working on matlab i'm having problems passing either cvxpy or cvxopt.

n = 1000; = 20; y = rand(n,1); = rand(n,i); cvx_begin variable x(n); variable lambda(i); minimize(sum_square(x-y)); subject     x == a*lambda;     lambda >= zeros(i,1);     lambda'*ones(i,1) == 1; cvx_end 

this tried python , cvxpy.

import numpy np cvxpy import *  # problem data. n = 100 = 20 np.random.seed(1) y = np.random.randn(n) = np.random.randn(n, i)  # construct problem. x = variable(n) lmbd = variable(i) objective = minimize(sum_squares(x - y)) constraints = [x == np.dot(a, lmbd),                lmbd <= np.zeros(itr),                np.sum(lmbd) == 1]  prob = problem(objective, constraints)  print("status:", prob.status) print("optimal value", prob.value) 

nonetheless, it's not working. of have idea how make work? i'm pretty sure problem in constraints. , nice have cvxopt.

i think got it, had 1 of constraints wrong =), added random seed number in order compare results , check in fact same in both languages. leave data here maybe useful someday ;)

matlab

rand('twister', 0); n = 100; = 20; y = rand(n,1); = rand(n,i); cvx_begin variable x(n); variable lmbd(i); minimize(sum_square(x-y)); subject     x == a*lmbd;     lmbd >= zeros(i,1);     lmbd'*ones(i,1) == 1; cvx_end 

cvxpy

import numpy np cvxpy import *  # random seed np.random.seed(0)  # problem data. n = 100 = 20 y = np.random.rand(n) # = np.random.rand(n, i)  # normal = np.random.rand(i, n).t  # in order test random numbers  # construct problem. x = variable(n) lmbd = variable(i) objective = minimize(sum_squares(x - y)) constraints = [x == a*lmbd,                lmbd >= np.zeros(i),                sum_entries(lmbd) == 1]  prob = problem(objective, constraints) result = prob.solve(verbose=true) 

cvxopt pending.....