SymPy rank different from NumPy matrix rank

Question

Given some SymPy matrix M

M = Matrix([
 [0.000111334436666596, 0.00114870370895408, -0.000328330524152990, 5.61388353859808e-6, -0.000464532588930332, -0.000969955779635878, 1.70579589853818e-5, -5.77891177019884e-6, -0.000186812539472235, -2.37115911398055e-5],
 [-0.00105346453420510, 0.000165063406707273, -0.00184449574409890, 0.000658080565333929, 0.00197652092300241, 0.000516180213512589, 9.53823860082390e-5, 0.000189858427211978, -3.80494288487685e-5, 0.000188984043643408],
 [-0.00102465075104153, -0.000402915220398109, 0.00123785300884241, -0.00125808154543978, 0.000126618511490838, 0.00185985865307693, 0.000123626008509804, 0.000211557638637554, 0.000407232404255796, 1.89851719447102e-5],
 [0.230813497584639, -0.209574389008468, 0.742275067362657, -0.202368828927654, -0.236683258718819, 0.183258819107153, 0.180335891933511, -0.530606389541138, -0.379368598768419, 0.334800403899511],
 [-0.00102465075104153, -0.000402915220398109, 0.00123785300884241, -0.00125808154543978, 0.000126618511490838, 0.00185985865307693, 0.000123626008509804, 0.000211557638637554, 0.000407232404255796, 1.89851719447102e-5],
 [0.00105346453420510, -0.000165063406707273, 0.00184449574409890, -0.000658080565333929, -0.00197652092300241, -0.000516180213512589, -9.53823860082390e-5, -0.000189858427211978, 3.80494288487685e-5, -0.000188984043643408],
 [0.945967255845168, -0.0468645728473480, 0.165423896937049, -0.893045423193559, -0.519428986944650, -0.0463256408085840, -0.0257001217930424, 0.0757328764368606, 0.0541336731317414, -0.0477734271777646],
 [-0.0273371493900004, -0.954100482348723, -0.0879282784854250, 0.100704543595514, -0.243312734473589, -0.0217088779350294, 0.900584332231093, 0.616061129532614, 0.0651163853434486, -0.0396603397583054],
 [0.0967584768347089, -0.0877680087304911, -0.667679934757176, -0.0848411039101494, -0.0224646387789634, -0.194501966574153, 0.0755161040544943, 0.699388977592066, 0.394125039254254, -0.342798611994521],
 [-0.000222668873333193, -0.00229740741790816, 0.000656661048305981, -1.12277670771962e-5, 0.000929065177860663, 0.00193991155927176, -3.41159179707635e-5, 1.15578235403977e-5, 0.000373625078944470, 4.74231822796110e-5]
 ])

I have calculated SymPy rank() and rref() of the matrix. Rank is 7 and rref() result is:

Matrix([
[1, 0, 0, 0, 0, 0, 0, -5.14556976678473, -3.72094268951566,  3.48581267477014],
[0, 1, 0, 0, 0, 0, 0, -5.52930150663022, -4.02230308325653,  3.79193678096199],
[0, 0, 1, 0, 0, 0, 0,  2.44893308665325,  1.83777402439421, -1.87489784909824],
[0, 0, 0, 1, 0, 0, 0, -7.33732284392352, -5.25036238623229,  4.97256759287563],
[0, 0, 0, 0, 1, 0, 0,  5.48049237370489,  3.90091366576548, -3.83642187384021],
[0, 0, 0, 0, 0, 1, 0, -10.6826798792866, -7.56560803870182,  7.45974067056387],
[0, 0, 0, 0, 0, 0, 1, -3.04726210012149, -2.66388837034592,  2.48327234504403],
[0, 0, 0, 0, 0, 0, 0,                 0,                 0,                 0],
[0, 0, 0, 0, 0, 0, 0,                 0,                 0,                 0],
[0, 0, 0, 0, 0, 0, 0,                 0,                 0,                 0]])

Weird thing is that if I calculate rank with either NumPy or MATLAB I get value 6 and calculating rref with MATLAB I get the expected result - last 4 rows are all zero (instead of only last 3).

Does any one know where does this difference comes from and why am I unable to get correct results with SymPy? I know that rank 6 is correct because it is system of the equations where some linear dependency exist.

score 3 · Accepted Answer · answered Dec 15 '18 at 13:54

Looking at the eigenvalues of your matrix, the rank is indeed 6:

array([ 1.14550481e+00+0.00000000e+00j, -1.82137718e-01+6.83443168e-01j,
   -1.82137718e-01-6.83443168e-01j,  2.76223053e-03+0.00000000e+00j,
   -3.51138883e-04+8.61508469e-04j, -3.51138883e-04-8.61508469e-04j,
    5.21160131e-17+0.00000000e+00j, -2.65160469e-16+0.00000000e+00j,
   -2.67753616e-18+9.70937977e-18j, -2.67753616e-18-9.70937977e-18j])

With the sympy version I have, I get even a rank of 8, compared to the rank 6 that numpy returns.

But actually, Sympy cannot solve the eigenvalues of this matrix due to the size of the matrix (probably related to SymPy could not compute the eigenvalues of this matrix).

So one of them, Sympy, is trying to solve symbolically the equations and find the rank (based on imperfect floating point numbers), whereas the other one, numpy, uses approximations (lapack IIRC) to find the eigenvalues. By having an adequate threshold, numpy finds the proper rank, but it could have said differently with a different threshold. Sympy tried to find the rank based on an approximate system of a perfect 6 rank system and finds that it is of rank 7 or 8. It's not surprising due to the floating point difference (Sympy moves to integers to try to find the eigenvalues, for instance, instead of staying in floating point realm).

I understand now where the problem is coming from. Are you aware of the possible solution? I tried adapting/modifying existing Python `rref` functions from the internet but up to this point did not have any luck finding a real solution for it. — carobnodrvo, Dec 15 '18 at 15:23
What is the objective of getting the rank? Unfortunately, the fact that the rows/columns are not really colinear in the floating point case doesn't make a symbolic solution easy to use. — Matthieu Brucher, Dec 15 '18 at 15:24
Well, I am not actually trying to compute the rank but instead to reduce rows of the presented matrix (and other similar matrices). — carobnodrvo, Dec 15 '18 at 15:26

SymPy rank different from NumPy matrix rank

1 Answers1