Updated: Aug 5, 2021
Matyas Function is usually evaluated on the square xi∈ [-10, 10], for all i = 1, 2.
The function has one global minimum f(𝑥∗)=0 at 𝑥∗ =(0,0)
The function is continuous.
The function is convex.
The function is defined on 2-D space.
The function is unimodal.
The function is differentiable.
The function is non-separable.
The function has no local minima except the global one
% Please forward any comments or bug reports in chat Copyright 2021. INDUSMIC PRIVATE LIMITED.THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY FOR THE USE OF THIS PROGRAM. If software is modified to produce derivative works, such modified software should be clearly marked. Additionally, user can redistribute it and/or modify it under the terms of the GNU General Public License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. See the GNU General Public License for more details. % for any support connect with us on email@example.com # Author: Dhanishtha Sharma import sympy import math from sympy import symbols from sympy import * from sympy.plotting import plot3d import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D %matplotlib notebook plt.rcParams['figure.figsize'] = (6,4) plt.rcParams['figure.dpi']=150 fig=plt.figure() ax=fig.add_subplot(111,projection='3d') x1,x2=symbols('x1 x2') plot3d(0.26*((x1**2)+(x2**2))-(0.48*x1*x2)) ,(x1,-10,10,2),(x2,-10,10,2)
 Jamil, Momin, and Xin-She Yang. "A literature survey of benchmark functions for global optimization problems." International Journal of Mathematical Modelling and Numerical Optimization 4.2 (2013): 150-194.