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
.
MATLAB
An implementation of the Rosenbrock Function with MATLAB is provided below.
The function can be represented in Latex as follows:
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