Convex Optimization. Mikhail Moklyachuk
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Название: Convex Optimization

Автор: Mikhail Moklyachuk

Издательство: John Wiley & Sons Limited

Жанр: Математика

Серия:

isbn: 9781119804086

isbn:

СКАЧАТЬ minimum (maximum) at the point then it is lower (upper) semicontinuous at the point;

      5 5) the function A for x ≠ 0, f(0) = +∞, is lower semicontinuous at the point 0. If we define the function at the point 0 arbitrarily, then it will remain lower semicontinuous.

      THEOREM 1.1.– Let f and g be lower semicontinuous functions. Then:

       – the function f + g is lower semicontinuous;

       – the function αf is lower semicontinuous for α ≥ 0 and it is upper semicontinuous for α ≤ 0;

       – the function f · g is lower semicontinuous for f ≥ 0, g ≥ 0;

       – the function 1/f is upper semicontinuous if f > 0;

       – the function max{f, g}, min{f, g} is lower semicontinuous;

       – the functions sup{fi} (inf{fi}) are lower (upper) semicontinuous, if the functions fi are lower (upper) semicontinuous.

      THEOREM 1.2.– (Weierstrass theorem) A lower (upper) semicontinuous on the interval [a, b] function f: ℝ → ℝ is bounded from below (from above) on [a, b] and attains the smallest (largest) value.

      THEOREM 1.3.– (Fermat’s theorem) If image is a point of local extremum of the differentiable at the point image function f(x), then image.

      Fermat’s theorem gives the first-order necessary condition for existence of a local extremum of the function f(x) at point image. The following theorems give the second-order necessary and sufficient conditions for the extremum.

      THEOREM 1.4.– (Necessary conditions of the second order) If image is a point of local minimum (maximum) of the function f(x), which has the second-order derivative at the point image then the following conditions hold true:

image

      THEOREM 1.5.– (Sufficient conditions of the second order) If the function f(x) has at a point image the second-order derivative and

image

      then image is the point of local minimum (maximum) of the function f(x).

      The necessary and sufficient conditions of the higher order of existence of an extremum of the function f(x) are given in the following theorems.

      THEOREM 1.6.– (Necessary conditions of higher order) If image is a point of local minimum (maximum) of the function f(x), which has at this point image the nth order derivative, then

image

      or

image

      for some m ≥ 1, 2mn.

      PROOF.– According to Taylor’s theorem, for a function which is n times differentiable at the point image we have

image

      If n = 1, then the assertion of the theorem is true as a result of Fermat’s theorem. Let n > 1, then

image

      or

image

      Let l be an odd number. Then the function image, uR can be expanded in a series by Taylor’s theorem

image

      The function g(u) has a derivative at point u = 0. Since image ∈ locmin f, then 0 ∈ locming g. According to Fermat’s theorem, image. Hence image. This contradicts the condition image. Therefore, the number l is even, l = 2m. According to Taylor’s theorem

image

      Since image, then image if image ∈ locmin f and image if image ∈ locmax f. □

      THEOREM 1.7.– (Sufficient conditions of higher order) If the function f(x) has a derivative of order n at point image and

image

      for some m ≥ 1, 2mn, then the function f(x) attains a local minimum (maximum) at the point image.

      PROOF.– Since image, then according to Taylor’s theorem

image

      If СКАЧАТЬ