# The way to solve a singular matrix

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Kommentiert: Aditya Agrawal am 8 Dez. 2020
Hi
There is any one know how the method to decompose the singular square matrix using Matlab. Someone told me the Matlab have something like a ready Forthran subroutine. Does anyone know how to use it in Matlab?
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### Antworten (3)

Mikhail am 22 Aug. 2014
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thanks Mikhail. but how can apply the svd to find the inverse of square singular matrix in order to solve the set of linear system.

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John D'Errico am 23 Aug. 2014
Bearbeitet: John D'Errico am 23 Aug. 2014
help pinv
Not much more to say, since you give very little info to help you on. Note that computing the inverse of a matrix is almost never recommended. The backslash operator is a better choice always than inv. But pinv is a good tool for this purpose, when backslash (and surely also inv) will fail.
A = ones(2);
A\[1;1]
Warning: Matrix is singular to working precision.
ans =
NaN
NaN
inv(A)*[1;1]
Warning: Matrix is singular to working precision.
ans =
Inf
Inf
pinv(A)*[1;1]
ans =
0.5
0.5
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Einat Shoval am 24 Dez. 2017
Thank you so much for this!! Was stuck on this for two days now until I found your answer :)
Aditya Agrawal am 8 Dez. 2020
Thankyou so much! I had the same issue and your solution works!

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Jess am 22 Mär. 2016
% Goal: solve A*x == b for x
% Set up some matrix A (I used a sparse matrix) -- do yourself
% Set up the vector b -- do yourself
% Perform SVD on A
[U,S,V] = svd(A);
% A == U*S*V' % Not needed, but you can check it yourself to confirm
% Calc number of singular values
s = diag(S); % vector of singular values
tolerance = max(size(A))*eps(max(s));
p = sum(s>tolerance);
% Define spaces
Up = U(:,1:p);
%U0 = U(:,p+1:Nx);
Vp = V(:,1:p);
%V0 = V(:,p+1:Nx);
%Sp = spdiags( s(1:p), 0, p, p );
SpInv = spdiags( 1.0./s(1:p), 0, p, p );
% Calc AInv such that x = AInv * b
AInv = Vp * SpInv * Up';
x = AInv * b; % DONE!
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Xin Shen am 10 Jul. 2019
When I tried your idea to solve my problem, I got an error "SVD does not support sparse matrices. Use SVDS to compute a subset of the singular values and vectors of a sparse matrix". Does SVDS work for your idea?

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