```markdown
**Description:**

The image presents a slide related to a family of standard linear programming (LP) problems. The problem is defined as follows:

- **Objective:** Minimize \( c^T x \)
- **Constraints:** 
  - \( Ax = b \)
  - \( x \succeq 0 \)

The matrix \( A \) is of size \( m \times 2m \). The parameter \( m \) ranges from 10 to 1000, and for each \( m \), 100 randomly generated instances are solved.

**Graph:**

- The graph shows the number of Newton iterations required as a function of \( m \).
- The x-axis is logarithmic, labeled as \( m \), ranging from \( 10^1 \) to \( 10^3 \).
- The y-axis shows the number of Newton iterations, ranging from 15 to 35.
- The plot indicates that the number of iterations grows very slowly as \( m \) increases over a 100:1 ratio.

**Additional Information:**

- The statement below the graph emphasizes the slow growth in the number of iterations with increasing \( m \).
- The slide is from a presentation by A. d'Aspremont on Convex Optimization, slide 17 of 36.
```
