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In mathematical optimization, the Rosenbrock function is a non-convex function, introduced by Howard H. Rosenbrock in 1960, which is used as a performance test problem for optimization algorithms. [1] It is also known as Rosenbrock's valley or Rosenbrock's banana function. The global minimum is inside a long, narrow, parabolic-shaped flat ...
In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems (MOP) are given. The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [ 1 ] Haupt et al. [ 2 ] and from Rody Oldenhuis software. [ 3 ]
The idea of Rosenbrock search is also used to initialize some root-finding routines, such as fzero (based on Brent's method) in Matlab. Rosenbrock search is a form of derivative-free search but may perform better on functions with sharp ridges. [6] The method often identifies such a ridge which, in many applications, leads to a solution. [7]
Figure 1: MCS algorithm (without local search) applied to the two-dimensional Rosenbrock function. The global minimum = is located at (,) = (,). MCS identifies a position with within 21 function evaluations. After additional 21 evaluations the optimal value is not improved and the algorithm terminates.
Test functions for optimization: Rosenbrock function — two-dimensional function with a banana-shaped valley; Himmelblau's function — two-dimensional with four local minima, defined by (,) = (+) + (+) Rastrigin function — two-dimensional function with many local minima
In mathematical optimization, the Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms. It is a typical example of non-linear multimodal function. It was first proposed in 1974 by Rastrigin [1] as a 2-dimensional function and has been generalized by Rudolph. [2]
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In mathematical optimization, Himmelblau's function is a multi-modal function, used to test the performance of optimization algorithms. The function is defined by: ...