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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 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.
Finding the extrema of functionals is similar to finding the maxima and minima of functions. The maxima and minima of a function may be located by finding the points where its derivative vanishes (i.e., is equal to zero). The extrema of functionals may be obtained by finding functions for which the functional derivative is equal to zero.
Fermat's method of adequality allowed him to determine the maxima and minima of functions and the tangents of curves. [13] Descartes's publication of La Géométrie in 1637, which introduced the Cartesian coordinate system , is considered to be the establishment of mathematical analysis.
In English, the full title can be translated as "A new method for maxima and minima, and for tangents, that is not hindered by fractional or irrational quantities, and a singular kind of calculus for the above mentioned." [2] It is from this title that this branch of mathematics takes the name calculus.
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.
Hasse diagram of the set P of divisors of 60, partially ordered by the relation "x divides y".The red subset = {1,2,3,4} has two maximal elements, viz. 3 and 4, and one minimal element, viz. 1, which is also its least element.
The sample maximum and minimum are the least robust statistics: they are maximally sensitive to outliers.. This can either be an advantage or a drawback: if extreme values are real (not measurement errors), and of real consequence, as in applications of extreme value theory such as building dikes or financial loss, then outliers (as reflected in sample extrema) are important.