I have noticed that if A is a NxN matrix and it has the inverse matrix. But what the inv() and pinv() function output is different. - My environment is Win7x64 SP1, Matlab R2012a, Cygwin Octave 3.6.4, FreeMat 4.2
Have a look at the examples from Octave:
A = rand(3,3)
A =
0.185987 0.192125 0.046346
0.140710 0.351007 0.236889
0.155899 0.107302 0.300623
pinv(A) == inv(A)
ans =
0 0 0
0 0 0
0 0 0
ans
result by running the same command above in Matlab.inv(A)*A
or A*inv(A)
, the result is identity 3x3 matrix in both Octave and Matlab.A*pinv(A)
and pinv(A)*A
are identity 3x3 matrix in Matlab and FreeMat.A*pinv(A)
is identity 3x3 matrix in Octave.pinv(A)*A
is not identity 3x3 matrix in Octave.I don't know the reason why inv(A) != pinv(A)
, I have considered the details of the element in the matrix. It seems to be the floating accuracy problem which causes this problem.
The 10+ digits after the dot point may be different like this:
6.65858991579923298331777914427220821380615200000000
element in inv(A)(1,1)
against
6.65858991579923209513935944414697587490081800000000
element in pinv(A)(1,1)
This question is quite old, but I'll answer it anyway because it appears almost on top in some google searches.
I'll use for my example the magic(N) function which returns an N-by-N magic square.
I'll create a 3x3 magic square M3, take the pseudoinverse PI_M3 and multiply them:
prompt_$ M3 = magic(3) , PI_M3 = pinv(M3) , M3 * PI_M3
M3 = 8 1 6 3 5 7 4 9 2 PI_M3 = 0.147222 -0.144444 0.063889 -0.061111 0.022222 0.105556 -0.019444 0.188889 -0.102778 ans = 1.0000e+00 -1.2212e-14 6.3283e-15 5.5511e-17 1.0000e+00 -2.2204e-16 -5.9952e-15 1.2268e-14 1.0000e+00
As you can see the answer is the identity matrix save for some rounding errors. I'll repeat the operation with a 4x4 magic square:
prompt_$ M4 = magic(4) , PI_M4 = pinv(M4) , M4 * PI_M4
M4 = 16 2 3 13 5 11 10 8 9 7 6 12 4 14 15 1 PI_M4 = 0.1011029 -0.0738971 -0.0613971 0.0636029 -0.0363971 0.0386029 0.0261029 0.0011029 0.0136029 -0.0113971 -0.0238971 0.0511029 -0.0488971 0.0761029 0.0886029 -0.0863971 ans = 0.950000 -0.150000 0.150000 0.050000 -0.150000 0.550000 0.450000 0.150000 0.150000 0.450000 0.550000 -0.150000 0.050000 0.150000 -0.150000 0.950000
The result is not the identity matrix, it means that the 4x4 magic square does not have an inverse. I can verify this by trying one of the rules of the Moore-Penrose pseudoinverse:
prompt_$ M4 * PI_M4 * M4
ans = 16.00000 2.00000 3.00000 13.00000 5.00000 11.00000 10.00000 8.00000 9.00000 7.00000 6.00000 12.00000 4.00000 14.00000 15.00000 1.00000
The rule A*B*A = A is satisfied. This shows that pinv returns the inverse matrix when it is available and the pseudoinverse when the inverse is not available. This is the reason why in some situations you get a small difference, just some rounding errors, and in other situations you get a bigger difference. To show it I'll get the inverse of both magic quadrants and subtract them from the pseudoinverse:
prompt_$ I_M3 = inv(M3) , I_M4 = inv(M4) , DIFF_M3 = PI_M3 - I_M3, DIFF_M4 = PI_M4 - I_M4
I_M3 = 0.147222 -0.144444 0.063889 -0.061111 0.022222 0.105556 -0.019444 0.188889 -0.102778 warning: inverse: matrix singular to machine precision, rcond = 1.30614e-17 I_M4 = 9.3825e+13 2.8147e+14 -2.8147e+14 -9.3825e+13 2.8147e+14 8.4442e+14 -8.4442e+14 -2.8147e+14 -2.8147e+14 -8.4442e+14 8.4442e+14 2.8147e+14 -9.3825e+13 -2.8147e+14 2.8147e+14 9.3825e+13 DIFF_M3 = 4.7184e-16 -1.0270e-15 5.5511e-16 -9.9226e-16 2.0470e-15 -1.0825e-15 5.2042e-16 -1.0270e-15 4.9960e-16 DIFF_M4 = -9.3825e+13 -2.8147e+14 2.8147e+14 9.3825e+13 -2.8147e+14 -8.4442e+14 8.4442e+14 2.8147e+14 2.8147e+14 8.4442e+14 -8.4442e+14 -2.8147e+14 9.3825e+13 2.8147e+14 -2.8147e+14 -9.3825e+13