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A new secant method for unconstrained optimization

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 نشر من قبل Stephen Vavasis
 تاريخ النشر 2008
  مجال البحث
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We present a gradient-based algorithm for unconstrained minimization derived from iterated linear change of basis. The new method is equivalent to linear conjugate gradient in the case of a quadratic objective function. In the case of exact line search it is a secant method. In practice, it performs comparably to BFGS and DFP and is sometimes more robust.



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