cvxopt

I wanted to optimize a function using ILP implementing by CVXOPT , GLPK in python. I wrote this code, but it gives me non integer solution especially 0.5. Could anyone help me? import math import numpy as np import cvxopt from cvxopt import glpk from cvxopt import matrix G = np.array([ [-1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., -1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., -1., 0., 1., -1., 0., 1., 1., 0., 0., -1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., -1., 0., 0., 0., 0.,

I need to convert a scipy sparse matrix to cvxopt's sparse matrix format, spmatrix, and haven't come across anything yet (the matrix is too big to be converted to dense, of course). Any ideas how to do this? Mtap1 The more robust answer is a combination of hpaulj's answer and OferHelman's answer . def scipy_sparse_to_spmatrix(A): coo = A.tocoo() SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape) return SP Defining the shape variable preserves the dimensionality of A on SP. I found that any zero columns ending the scipy sparse matrix would be lost without this

I am trying to combine cvxopt (an optimization solver) and PyMC (a sampler) to solve convex stochastic optimization problems . For reference, installing both packages with pip is straightforward: pip install cvxopt pip install pymc Both packages work independently perfectly well. Here is an example of how to solve an LP problem with cvxopt : # Testing that cvxopt works from cvxopt import matrix, solvers # Example from http://cvxopt.org/userguide/coneprog.html#linear-programming c = matrix([-4., -5.]) G = matrix([[2., 1., -1., 0.], [1., 2., 0., -1.]]) h = matrix([3., 3., 0., 0.]) sol = solvers

I want to further my real world semi definite programming optimization problem with a constraint on sum of absolute values. For example: abs(x1) + abs(x2) + abs(x3) <= 10. I have searched internet and documentation but could not find a way to represent this. I am using python and cvxopt module. Your constraint is equivalent to the following eight constraints: x1 + x2 + x3 <= 10 x1 + x2 - x3 <= 10 x1 - x2 + x3 <= 10 x1 - x2 - x3 <= 10 -x1 + x2 + x3 <= 10 -x1 + x2 - x3 <= 10 -x1 - x2 + x3 <= 10 -x1 - x2 - x3 <= 10 I haven't used cvxopt , so I don't know if there is an easier way to handle your

I'm trying to use the CVXOPT qp solver to compute the Lagrange Multipliers for a Support Vector Machine def svm(X, Y, c): m = len(X) P = matrix(np.dot(Y, Y.T) * np.dot(X, X.T)) q = matrix(np.ones(m) * -1) g1 = np.asarray(np.diag(np.ones(m) * -1)) g2 = np.asarray(np.diag(np.ones(m))) G = matrix(np.append(g1, g2, axis=0)) h = matrix(np.append(np.zeros(m), (np.ones(m) * c), axis =0)) A = np.reshape((Y.T), (1,m)) b = matrix([0]) print (A).shape A = matrix(A) sol = solvers.qp(P, q, G, h, A, b) print sol Here X is a 1000 X 2 matrix and Y has the same number of labels. The solver throws the following