```markdown
### Image Description

The image presents an optimization problem and a graph related to semidefinite programming (SDP).

#### Optimization Problem
- **Objective:** Minimize \(1^T x\)
- **Subject to:** \(A + \text{diag}(x) \succeq 0\)
- **Variables:**
  - \(A \in S^n\) (symmetric matrices)
  - \(x \in \mathbb{R}^n\)

#### Experiment Details
- \(n = 10, \ldots, 1000\)
- For each \(n\), 100 randomly generated instances are solved.

#### Graph
- **Axes:**
  - X-axis: \(n\) (logarithmic scale from \(10^1\) to \(10^3\))
  - Y-axis: Newton iterations (ranging from 15 to 35)
- **Data:**
  - The plot shows the number of Newton iterations required for convergence across different values of \(n\).
  - The graph displays a line with circular markers and error bars indicating variability.

#### Source
- A. d’Aspremont. Convex Optimization M2. Slide 33/36
```
