Rosenbrock Function
Mathematical Definition
\[f(\textbf{x})=f(x_1, ..., x_n)=\sum_{i=1}^{n-1}[b (x_{i+1} - x_i^2)^ 2 + (a - x_i)^2]\]In this formula, the parameters $a$ and $b$ are constants and are generally set to $a=1$ and $b=100$.
Plots
The contour of the function is as presented below:
Description and Features
- The function is continuous.
- The function is convex.
- The function is defined on n-dimensional space.
- The function is multimodal.
- The function is differentiable.
- The function is non-separable.
Input Domain
The function can be defined on any input domain but it is usually evaluated on $x_i \in [-5, 10]$ for $i=1, …, n$ .
Global Minima
The function has one global minimum $f(\textbf{x}^{\ast})=0$ at $\textbf{x}^{\ast} = (1, …, 1)$.
Implementation
Python
For Python, the function is implemented in the benchmarkfcns package, which can be installed from command line with pip install benchmarkfcns
.
from benchmarkfcns import rosenbrock
print(rosenbrock([[0, 0, 0],
[1, 1, 1]]))
MATLAB
An implementation of the Rosenbrock Function with MATLAB is provided below.
% Computes the value of the Rosenbrock benchmark function.
% SCORES = ROSENBROCKFCN(X) computes the value of the Rosenbrock function
% at point X. ROSENBROCKFCN accepts a matrix of size M-by-N and returns a
% vetor SCORES of size M-by-1 in which each row contains the function value
% for the corresponding row of X.
% For more information please visit:
% https://en.wikipedia.org/wiki/Rosenbrock_function
%
% Author: Mazhar Ansari Ardeh
% Please forward any comments or bug reports to mazhar.ansari.ardeh at
% Google's e-mail service or feel free to kindly modify the repository.
function scores = rosenbrockfcn(x)
scores = 0;
n = size(x, 2);
assert(n >= 1, 'Given input X cannot be empty');
a = 1;
b = 100;
for i = 1 : (n-1)
scores = scores + (b * ((x(:, i+1) - (x(:, i).^2)) .^ 2)) + ((a - x(:, i)) .^ 2);
end
end
The function can be represented in Latex as follows:
f(\textbf{x})=f(x_1, ..., x_n)=\sum_{i=1}^{n-1}[b (x_{i+1} - x_i^2)^ 2 + (a - x_i)^2]
Acknowledgement
Tobias Völk kindly contributed to the correctness of this document.
References:
- http://www.sfu.ca/~ssurjano/rosen.html
- https://en.wikipedia.org/wiki/Test_functions_for_optimization
- Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modelling and Numerical Optimisation}, Vol. 4, No. 2, pp. 150–194 (2013), arXiv:1308.4008