Easom Function
Mathematical Definition
\[f(x,y)=−cos(x)cos(y) exp(−(x − \pi)^2−(y − \pi)^2)\]Plots
The contour of the function is as presented below:
Description and Features
- The function is continuous.
- The function is not convex.
- The function is defined on 2-dimensional space.
- The function is multimodal.
- The function is differentiable.
- The function is non-separable.
- The function is non-scalable.
Input Domain
The function can be defined on any input domain but it is usually evaluated on $x \in [-100, 100]$ and $y \in [-100, 100]$ .
Global Minima
The function has four global minima $f(x^{\ast}, y^{\ast})=-1$ at $(x^{\ast}, y^{\ast}) = (\pi,\pi)$.
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 easom
print(easom([[0, 0],
[1, 1]]))
MATLAB
An implementation of the Easom Function with MATLAB is provided below.
% Computes the value of the Easom benchmark function.
% SCORES = EASOMFCN(X) computes the value of the Easom function at point X.
% EASOMFCN accepts a matrix of size M-by-2 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/Test_functions_for_optimization
%
% 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 = easomfcn(x)
n = size(x, 2);
assert(n == 2, 'The Easom''s function is only defined on a 2D space.')
X = x(:, 1);
Y = x(:, 2);
scores = -cos(X) .* cos(Y) .* exp(-( ((X - pi) .^2) + ((Y - pi) .^ 2)) );
end
The function can be represented in Latex as follows:
f(x,y)=−cos(x)cos(y) exp(−(x − \pi)^2−(y − \pi)^2)
Acknowledgement
Tobias Völk kindly contributed to the correctness of this document.
References:
- http://www.sfu.ca/~ssurjano/easom.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
- http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO_files/Page1361.htm