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On Jorge Nocedal's , Page 501, "This property alone is enough to make many unconstrained minimization algorithms such as quasi-Newton and conjugate gradient perform poorly. Newton’s method, on the other hand, is not sensitive to ill conditioning of the Hessian". Can any one give a more detailed analysis?

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  • $\begingroup$ Hi: Newton's method doesn't use any information regarding the second derivative so that's why it's insensitive to it. It's a method that only relies on the first derivative. This is not the case for quasi-Newton and conjugate gradient methods. $\endgroup$
    – mlofton
    Jan 8 '19 at 6:23
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    $\begingroup$ @mlofton: ?????????????????????? $\endgroup$ Jan 8 '19 at 9:26
  • $\begingroup$ @kjeitil b halvorsen: I thought what I said was the case. if not, I'll delete comment. $\endgroup$
    – mlofton
    Jan 9 '19 at 15:37
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    $\begingroup$ @mlofton Newton's method is a second order optimization method, which means it does in fact use the second derivative and as such computes (and inverts) the hessian directly. Newton-CG on the other hand solves a system of equations to 'invert' the hessian. All second order methods use the Hessian or at least some approximation. $\endgroup$
    – Avelina
    Jun 28 at 15:59
  • $\begingroup$ oops: my mistake. I thought it just used the first derivative. thanks for heads up. should I delete my totally wrong comment-statement. $\endgroup$
    – mlofton
    Jun 28 at 18:11
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"An ill conditioned hessian means that the gradient is changing direction rapidly; this causes a problem because our method is only looking at the gradient at one point.􏰂 Newton's method is solving this problem because it looks at the hessian and is taking the rate of change of the gradient into account."

source: https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/recitations/rec3.pdf

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