**Mathematical Definition**

**Input Domain**

The function can be defined on any input domain but it is usually evaluated on xi ∈ [−5, 10], xi ∈ [−5, 10] for i=1,…, n.

**Global Minima**

The function has one global minimum f(x∗)=0 at x∗=(1,…,1).

**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.

**Python Implementation**

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*
from matplotlib import pyplot as plt
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
def f(x1, x2): return 100*(x2-x1**2)**2+(x1-1)**2
x1 = np.linspace(-5, 10)
x2 = np.linspace(-5, 10)
X1, X2 = np.meshgrid(x1, x2)
F = f(x1, x2)
plt.contour(X1, X2, f(X1, X2))
def plotter(E, A):
fig = plt.figure(figsize = [12, 8])
ax = plt.axes(projection='3d')
ax.plot_surface(X1, X2, f(X1, X2), cmap='jet', alpha=0.8)
ax.plot_wireframe(X1, X2, f(X1, X2), rcount=15, ccount=15)
ax.view_init(elev=E, azim=A)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('f(X, Y)')
ax.contourf(x1, x2, f(X1, X2))
print("solution 2")
plotter(45, 45)
from ipywidgets import interactive
iplot = interactive(plotter, E = (-90, 90, 5),
A = (-90, 90, 5))
iplot

**References:**

[1] 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.

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