Suppose $A\in\mathbb{R}^{n\times n}$ is a symmetric, positive definite matrix. $A$ is big enough that it's expensive to solve $Ax=b$ directly.

Is there an iterative algorithm for finding the smallest eigenvalue of $A$ that doesn't involve inverting $A$ in each iteration?

That is, I'd have to use an iterative algorithm like conjugate gradients to solve $Ax=b$, so repeatedly applying $A^{-1}$ seems like an expensive "inner loop." I only need a single eigenvector.


  • 1
    $\begingroup$ Have you tried using the Cholesky decomposition? You'd have to factor $A$ into $L L^T$ with $L$ being a triangular matrix. Once you have the factorization (you only do this once) you can use it in every iteration to solve the system very fast by back and forward substitution. $\endgroup$ – Juan M. Bello-Rivas Jan 2 '16 at 21:54
  • $\begingroup$ Is A a sparse matrix? $\endgroup$ – Tolga Birdal Jan 2 '16 at 21:55
  • $\begingroup$ $A$ has some block structure, but I'd prefer not to mess with it if I don't have to -- so I was looking into "matrix free methods." The "LOBPCG" algorithm has some promise, I think! @Juan, the Cholesky factorization is still quite expensive. $\endgroup$ – Justin Solomon Jan 2 '16 at 21:57
  • $\begingroup$ If you are using matlab or octave use the eigs-routine. It is an iterative method. There are options to specify which eigenvalue you want, e.g. smallest real. $\endgroup$ – sebastian_g Jan 3 '16 at 12:17
  • $\begingroup$ I understand and am indeed using eigs in matlab. But if you specify options like "sm" in eigs, then it requires the inverse of $A$ rather than $A$. Check out the table in the documentation: mathworks.com/help/matlab/ref/eigs.html $\endgroup$ – Justin Solomon Jan 3 '16 at 16:35
  1. Compute the largest-magnitude eigenvalue $\lambda_{max}$ of $A$ (with, say, eigs('lm')).

  2. Then compute the largest magnitude (negative) eigenvalue $\hat{\lambda}_{max}$ of $M = A - \lambda_{max}I$ (again, through a standard call to eigs('lm')).

  3. Observe that $\hat{\lambda}_{max} + \lambda_{\max} = \lambda_{min}(A)$. The reason why this holds is explained here.

  4. Find your eigenvector $v$ by solving $(A - \lambda_{min} I) v = 0$.


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