Singularity in matrix when inverting in Matlab As data I get a matrix A but in my algorithm I need to work on its inverse. What I do is:
C = inv(A) + B;

Then in another line I update A. In the next cycles I also need (updated) A inverse, again for this algorithm. And so on. In the later cycles I get this:
Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = 1.425117e-019

or this:   
Warning: Matrix is singular to working precision.

or this:
Warning: Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN.

Can you help me how to avoid such singularity? Matrix is squared always.

It definitely depends on what you are doing and what matrix A represents (in some cases there might be programming error causing A become singular). But if the singularity of A is unavoidable, you can use the Moore-Penrose pseudoinverse as an alternative to inverse matrix which has most of the properties of an inverse matrix (See it in wiki).
The equivalent command in Matlab would be pinv.
