<p><code>inv()</code> is certainly slower than <code>\</code> unless you have multiple right hand side vectors to solve for. However, the advice from MathWorks regarding inaccuracy is due to a overly conservative bound in a numerical linear algebra result. In other words, <code>inv()</code> is NOT inaccurate. The link elaborates further : <a href="http://arxiv.org/abs/1201.6035" rel="nofollow">http://arxiv.org/abs/1201.6035</a> </p>

<blockquote>
  <p>Several widely-used textbooks lead the reader to believe that solving a linear system of equations Ax = b by multiplying the vector b by a computed inverse inv(A) is inaccurate. Virtually all other textbooks on numerical analysis and numerical linear algebra advise against using computed inverses without stating whether this is accurate or not. In fact, under reasonable assumptions on how the inverse is computed, x = inv(A)*b is as accurate as the solution computed by the best backward-stable solvers.</p>
</blockquote>
Why is Matlab's inv slow and inaccurate?
<p>I read at a few places (in the doc and in this blog post : <a href="http://blogs.mathworks.com/loren/2007/05/16/purpose-of-inv/" rel="noreferrer">http://blogs.mathworks.com/loren/2007/05/16/purpose-of-inv/</a> ) that the use of inv in Matlab is not recommended because it is slow and inaccurate.</p>

<p>I am trying to find the reason of this inaccuracy. As of now, Google did not give m interesting result, so I thought someone here could guide me.</p>

<p>Thanks !</p>