'solve' fucntion works badly on ill-conditioned matrices (atleast numpy works better) (Feature #2655)

Related issue: #2643

import numpy as np
import numpy.linalg as la
import cv2

A = np.float64([[8.193152026529024541e+07, 3.256524952090702206e+07, 1.280089998874972807e+05],
                [3.256524952090702206e+07, 1.432970570196110941e+07, 5.087970962876405974e+04],
                [1.280089998874972807e+05, 5.087970962876405974e+04, 2.000000000000000000e+02]])
B = np.float64( [3.543302075289976597e+07, 1.408204120224751532e+07, 5.536028820708069543e+04] )

print la.svd(A)[1]

x = la.solve(A, B)
x1 = cv2.solve(A, B, flags=cv2.DECOMP_QR)[1].ravel()
x2 = cv2.solve(A, B, flags=cv2.DECOMP_LU)[1].ravel()

print la.norm( A.dot(x) - B )  # 0.000149714585044  
print la.norm( A.dot(x1) - B ) # 0.0858401225198    
print la.norm( A.dot(x2) - B ) # 219.692813301