```markdown
The image contains a slide from a presentation on convex optimization, specifically addressing the norm approximation problem. The main focus is on minimizing the expression \(\|Ax - b\|\), with the constraint \(y = Ax - b\).

The slide outlines the derivation of the dual problem using the conjugate of the norm. The function \(g(\nu)\) is defined as:

\[ 
g(\nu) = \inf_{x, y} ( \|y\| + \nu^T y - \nu^T Ax + b^T \nu) 
\]

This is further simplified to:

\[
g(\nu) = 
\begin{cases} 
b^T \nu + \inf_y (\|y\| + \nu^T y) & \text{if } A^T \nu = 0 \\
-\infty & \text{otherwise} 
\end{cases} 
\]

And then to:

\[
g(\nu) = 
\begin{cases} 
b^T \nu & \text{if } A^T \nu = 0, \|\nu\|_* \leq 1 \\
-\infty & \text{otherwise} 
\end{cases} 
\]

The dual of the norm approximation problem is stated as:

- **Maximize** \(b^T \nu\)
- **Subject to** \(A^T \nu = 0\), \(\|\nu\|_* \leq 1\)

The slide references "see page 7" and credits A. d'Aspremont for "Convex Optimization M2" on page 28 of 49.
```