```markdown
### Image Description

The image consists of two plots related to convex optimization examples, specifically for an inequality form linear program (LP) with 100 inequalities and 50 variables.

#### Left Plot
- **Title:** Not explicitly titled.
- **Axes:**
  - X-axis: Labeled "Newton iterations" ranging from 0 to 80.
  - Y-axis: Labeled "duality gap" on a logarithmic scale from \(10^{-6}\) to \(10^2\).
- **Content:** 
  - Three step-like curves representing different values of \(\mu\): 50, 150, and 2.
  - The curves show how the duality gap decreases with Newton iterations.

#### Right Plot
- **Title:** Not explicitly titled.
- **Axes:**
  - X-axis: Labeled "\(\mu\)" ranging from 0 to 200.
  - Y-axis: Labeled "Newton iterations" ranging from 0 to 140.
- **Content:** 
  - A curve indicating the relationship between \(\mu\) and the number of Newton iterations, displaying a relatively stable trend for \(\mu \geq 10\).

### Additional Information
- **Text Notes:**
  - Starts with \(x\) on the central path (\(t^{(0)} = 1\), duality gap 100).
  - Terminates when \(t = 10^8\) (gap \(10^{-6}\)).
  - Centering uses Newton's method with backtracking.
  - Total number of Newton iterations not very sensitive for \(\mu \geq 10\).

- **Source:** A. d'Aspremont. Convex Optimization M2, Slide 15/36.
```
