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Math Help - Escaping from a saddle point

  1. #1
    Apr 2009

    Escaping from a saddle point


    I have an minimization problem in which by construction I sometimes need to start from a saddle point. To escape from this saddle point a possible solution would be to take as a descent direction the direction pointed by the eigenvector associated to the smallest negative eigenvalue of the Hessian matrix. This is unfortunately very computationally expensive.

    To find a "good" direction I thought of drawing random unit vectors x_i and look at the value \mathbf x_i^T H \mathbf x_i, where H is the Hessian matrix. If I find a negative value, I use the vector \mathbf x_i as a descent direction and I know it's a good escape direction other wise i take the one with the smallest value and just test if i can minimize more.

    My intuition that it might work is based on the fact that:
    \mathbf x^T H \mathbf x = \sum_k \lambda_k (\mathbf u_k^T \mathbf x)^2
    where \lambda_k and \mathbf u_k are the eigenvalues and corresponding eigenvector of H. So if \mathbf x lies mostly in the subspace corresponding to the negative eigenvalues, i will get a negative or at least a small value and x is a good direction.

    Does any one knows about some useful references about this approach ?
    Or any useful comment ?


    Last edited by AlexisM; December 9th 2011 at 03:09 AM. Reason: cleaning notations
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