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We can therefore use Newton's method on its derivative ′ to find solutions to ′ =, also known as the critical points of . These solutions may be minima, maxima, or saddle points; see section "Several variables" in Critical point (mathematics) and also section "Geometric interpretation" in this article.
Maxima and minima x 2: Unique global minimum at x = 0. x 3: No global minima or maxima. Although the first derivative (3x 2) is 0 at x = 0, this is an inflection point. (2nd derivative is 0 at that point.) Unique global maximum at x = e. (See figure at right) x −x: Unique global maximum over the positive real numbers at x = 1/e.
Let the minima found during each bi-directional line search be {+, + =, …, + =}, where is the initial starting point and is the scalar determined during bi-directional search along . The new position ( x 1 {\textstyle x_{1}} ) can then be expressed as a linear combination of the search vectors i.e. x 1 = x 0 + ∑ i = 1 N α i s i {\textstyle ...
In numerical analysis, a quasi-Newton method is an iterative numerical method used either to find zeroes or to find local maxima and minima of functions via an iterative recurrence formula much like the one for Newton's method, except using approximations of the derivatives of the functions in place of exact derivatives.
In calculus, a derivative test uses the derivatives of a function to locate the critical points of a function and determine whether each point is a local maximum, a local minimum, or a saddle point. Derivative tests can also give information about the concavity of a function.
Newton's method is a special case of a curve-fitting method, in which the curve is a degree-two polynomial, constructed using the first and second derivatives of f. If the method is started close enough to a non-degenerate local minimum (= with a positive second derivative), then it has quadratic convergence.
Fermat's theorem is central to the calculus method of determining maxima and minima: in one dimension, one can find extrema by simply computing the stationary points (by computing the zeros of the derivative), the non-differentiable points, and the boundary points, and then investigating this set to determine the extrema.
Forgo the benefits of a clever descent direction by setting = (), and use line search to find a suitable step-size , such as one that satisfies the Wolfe conditions. A more economic way of choosing learning rates is backtracking line search , a method that has both good theoretical guarantees and experimental results.