```markdown
## Unconstrained minimization

**Objective:**
- Minimize \( f(x) \)

**Assumptions:**
- \( f \) is convex, twice continuously differentiable (hence \( \text{dom } f \) is open).
- Assume optimal value \( p^\star = \inf_x f(x) \) is attained (and finite).

**Unconstrained minimization methods:**
- Produce a sequence of points \( x^{(k)} \in \text{dom } f, \, k = 0, 1, \ldots \) with
  \[
  f(x^{(k)}) \to p^\star
  \]

- Can be interpreted as iterative methods for solving the optimality condition:
  \[
  \nabla f(x^\star) = 0
  \]

*Source: A. d'Aspremont. Convex Optimization M2.*
```
