```markdown
### Image Description

The slide titled "Examples" outlines several optimization concepts and problems. Key points include:

- **Types of Problems:**
  - LP (Linear Programming)
  - QP (Quadratic Programming)
  - QCQP (Quadratically Constrained Quadratic Programming)
  - GP (Geometric Programming)

- **Entropy Maximization:**
  - Problem formulation involves minimizing the function \(\sum_{i=1}^{n} x_i \log x_i\).
  - Subject to constraints \(Fx \preceq g\) and \(Ax = b\).
  - The domain of \(f_0\) is \(\mathbb{R}^n_{++}\).

- **Differentiability:**
  - May require reformulating the problem, such as using piecewise-linear minimization or \(\ell_\infty\)-norm approximation via LP.

- **SDPs and SOCPs:**
  - Better handled as problems with generalized inequalities, with a note to see further details later.

The slide is part of a presentation by A. d'Aspremont on Convex Optimization (M2), as indicated at the bottom left corner.
```
