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Finding global maxima and minima is the goal of mathematical optimization. If a function is continuous on a closed interval, then by the extreme value theorem, global maxima and minima exist. Furthermore, a global maximum (or minimum) either must be a local maximum (or minimum) in the interior of the domain, or must lie on the boundary of the ...
The critical points of Lagrangians occur at saddle points, rather than at local maxima (or minima). [ 4 ] [ 17 ] Unfortunately, many numerical optimization techniques, such as hill climbing , gradient descent , some of the quasi-Newton methods , among others, are designed to find local maxima (or minima) and not saddle points.
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.
Fermat's theorem (stationary points), about local maxima and minima of differentiable functions; Fermat's principle, about the path taken by a ray of light; Fermat polygonal number theorem, about expressing integers as a sum of polygonal numbers; Fermat's right triangle theorem, about squares not being expressible as the difference of two ...
Perhaps the best-known example of the idea of locality lies in the concept of local minimum (or local maximum), which is a point in a function whose functional value is the smallest (resp., largest) within an immediate neighborhood of points. [1]
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.
a local maximum (maximal turning point or relative maximum) is one where the derivative of the function changes from positive to negative; Saddle points (stationary points that are neither local maxima nor minima: they are inflection points. The left is a "rising point of inflection" (derivative is positive on both sides of the red point); the ...
The conditions that distinguish maxima, or minima, from other stationary points are called 'second-order conditions' (see 'Second derivative test'). If a candidate solution satisfies the first-order conditions, then the satisfaction of the second-order conditions as well is sufficient to establish at least local optimality.