```markdown
The image contains two plots related to Newton methods in convex optimization.

1. **Top Plot: Newton Method Applied to Dual Problem**
   - **Title**: "Newton method applied to dual problem (requires \( A^T \nu^{(0)} \succ 0 \))"
   - **Axes**:
     - X-axis: \( k \) (iteration index), ranging from 0 to 10.
     - Y-axis: \( p^* - g(\nu^{(k)}) \), ranging from \( 10^{-10} \) to \( 10^5 \), plotted on a logarithmic scale.
   - **Description**: The plot shows a decreasing trend in the error \( p^* - g(\nu^{(k)}) \) as the iterations progress, indicating convergence.

2. **Bottom Plot: Infeasible Start Newton Method**
   - **Title**: "Infeasible start Newton method (requires \( x^{(0)} \succ 0 \))"
   - **Axes**:
     - X-axis: \( k \) (iteration index), ranging from 0 to 25.
     - Y-axis: \( \| r(x^{(k)}, \nu^{(k)}) \|_2 \), ranging from \( 10^{-15} \) to \( 10^{10} \), plotted on a logarithmic scale.
   - **Description**: The plot shows a decreasing trend in the residual norm \( \| r(x^{(k)}, \nu^{(k)}) \|_2 \) over iterations, indicating convergence.

**Source**: A. d'Aspremont. Convex Optimization M2. Page 47/53.
```
