Rosenbrock Optimization Landscape

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

colors = ['#0a0a0f', '#7f1d1d', '#ef4444', '#fbbf24']
cmap = LinearSegmentedColormap.from_list('fire', colors, N=256)

X = np.linspace(-2, 2, 100)
Y = np.linspace(-1, 3, 100)
X, Y = np.meshgrid(X, Y)
Z = (1 - X)**2 + 100*(Y - X**2)**2  # Rosenbrock
Z = np.log10(Z + 1)  # Log scale for visualization

fig = plt.figure(figsize=(10, 8), facecolor='#0a0a0f')
ax = fig.add_subplot(111, projection='3d', facecolor='#0a0a0f')

surf = ax.plot_surface(X, Y, Z, cmap=cmap, linewidth=0, antialiased=True)

ax.set_xlabel('X', color='white', fontsize=10)
ax.set_ylabel('Y', color='white', fontsize=10)
ax.set_zlabel('log₁₀(f+1)', color='white', fontsize=10)
ax.set_title('Rosenbrock Optimization Landscape', color='white', fontsize=14, fontweight='bold', pad=20)

ax.tick_params(colors='#64748b', labelsize=8)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('#1e293b')
ax.yaxis.pane.set_edgecolor('#1e293b')
ax.zaxis.pane.set_edgecolor('#1e293b')

ax.view_init(elev=30, azim=45)
plt.tight_layout()
plt.show()