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  2. Arg max - Wikipedia

    en.wikipedia.org/wiki/Arg_max

    The unnormalised sinc function (red) has arg min of {−4.49, 4.49}, approximately, because it has 2 global minimum values of approximately −0.217 at x = ±4.49. However, the normalised sinc function (blue) has arg min of {−1.43, 1.43}, approximately, because their global minima occur at x = ±1.43, even though the minimum value is the same.

  3. Maximum and minimum - Wikipedia

    en.wikipedia.org/wiki/Maximum_and_minimum

    In mathematical analysis, the maximum and minimum [a] of a function are, respectively, the greatest and least value taken by the function. Known generically as extremum , [ b ] they may be defined either within a given range (the local or relative extrema) or on the entire domain (the global or absolute extrema) of a function.

  4. Smooth maximum - Wikipedia

    en.wikipedia.org/wiki/Smooth_maximum

    Smoothmax of (−x, x) versus x for various parameter values. Very smooth for =0.5, and more sharp for =8. For large positive values of the parameter >, the following formulation is a smooth, differentiable approximation of the maximum function. For negative values of the parameter that are large in absolute value, it approximates the minimum.

  5. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). [1]

  6. Maximal function - Wikipedia

    en.wikipedia.org/wiki/Maximal_function

    Here, the supremum is taken over balls B in R n which contain the point x and |B| denotes the measure of B (in this case a multiple of the radius of the ball raised to the power n). One can also study the centred maximal function, where the supremum is taken just over balls B which have centre x. In practice there is little difference between ...

  7. Duality (optimization) - Wikipedia

    en.wikipedia.org/wiki/Duality_(optimization)

    Another condition in which the min-max and max-min are equal is when the Lagrangian has a saddle point: (x∗, λ∗) is a saddle point of the Lagrange function L if and only if x∗ is an optimal solution to the primal, λ∗ is an optimal solution to the dual, and the optimal values in the indicated problems are equal to each other. [18 ...

  8. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    For example, in this encoding ⁡ (,,) = (,,), since the third argument is the maximum. This can be generalized to multiple arg max values (multiple equal being the maximum) by dividing the 1 between all max args; formally 1/k where k is the number of arguments assuming the maximum.

  9. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    The extreme value theorem of Karl Weierstrass states that a continuous real-valued function on a compact set attains its maximum and minimum value. More generally, a lower semi-continuous function on a compact set attains its minimum; an upper semi-continuous function on a compact set attains its maximum point or view.