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Rosenbrock function : ウィキペディア英語版
Rosenbrock function

In mathematical optimization, the Rosenbrock function is a non-convex function used as a performance test problem for optimization algorithms introduced by Howard H. Rosenbrock in 1960. It is also known as Rosenbrock's valley or Rosenbrock's banana function.
The global minimum is inside a long, narrow, parabolic shaped flat valley. To find the valley is trivial. To converge to the global minimum, however, is difficult.
The function is defined by
f(x, y) = (a-x)^2 + b(y-x^2)^2
It has a global minimum at (x, y)=(a, a^2), where f(x, y)=0. Usually a = 1 and b = 100.
==Multidimensional generalisations==

Two variants are commonly encountered. One is the sum of N/2 uncoupled 2D Rosenbrock problems,
: f(\mathbf) = f(x_1, x_2, \dots, x_N) = \sum_^ \left(- x_)^2
+ (x_ - 1)^2 \right ).
This variant is only defined for even N and has predictably simple solutions.
A more involved variant is
: f(\mathbf) = \sum_^ 100 (x_ - x_i^2 )^2 + (1-x_i)^2 \quad \mbox \quad \mathbf = (\ldots, x_N ) \in \mathbb^N.
This variant has been shown to have exactly one minimum for N=3 (at (1, 1, 1)) and exactly two minima for 4 \le N \le 7—the global minimum of all ones and a local minimum near (x_1, x_2, \dots, x_N) = (-1, 1, \dots, 1). This result is obtained by setting the gradient of the function equal to zero, noticing that the resulting equation is a rational function of x. For small N the polynomials can be determined exactly and Sturm's theorem can be used to determine the number of real roots, while the roots can be bounded in the region of |x_i| < 2.4. For larger N this method breaks down due to the size of the coefficients involved.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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