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- W2952429416 abstract "Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors." @default.
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- W2952429416 date "2012-06-18" @default.
- W2952429416 modified "2023-09-27" @default.
- W2952429416 title "Quasi-Newton Methods: A New Direction" @default.
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