MATLAB style quadprog from CVXOPT qp

quadprog.py

from cvxopt import matrix as cvxmat, sparse, spmatrix
from cvxopt.solvers import qp, options
import numpy as np

def quadprog(H, f, Aeq, beq, lb, ub):
    """
    minimize:
            (1/2)*x'*H*x + f'*x
    subject to:
            Aeq*x = beq 
            lb <= x <= ub
    """
    P, q, G, h, A, b = _convert(H, f, Aeq, beq, lb, ub)
    results = qp(P, q, G, h, A, b)

    # Convert back to NumPy matrix
    # and return solution
    xstar = results['x']
    return np.matrix(xstar)

def _convert(H, f, Aeq, beq, lb, ub):                                                                                  
    """
    Convert everything to                                                                                              
    cvxopt-style matrices                                                                                              
    """ 
    P = cvxmat(H)                                                                                                      
    q = cvxmat(f)
    if Aeq is None:                                                                                                    
        A = None                                                                                                       
    else: 
        A = cvxmat(Aeq)                                                                                                
    if beq is None:                                                                                                    
        b = None                                                                                                       
    else: 
        b = cvxmat(beq)                                                                                                
    
    n = lb.size
    G = sparse([-speye(n), speye(n)])                                                                                  
    h = cvxmat(np.vstack([-lb, ub]))                                                                                      
    return P, q, G, h, A, b 

def speye(n):
    """Create a sparse identity matrix"""
    r = range(n)
    return spmatrix(1.0, r, r)