```markdown
**Description:**

The image presents a quadratic optimization problem in \(\mathbb{R}^2\). The function is defined as:

\[ f(x) = (1/2)(x_1^2 + \gamma x_2^2) \quad (\gamma > 0) \]

The optimization uses exact line search, starting at \(x^{(0)} = (\gamma, 1)\). The iterative update rules for \(x_1\) and \(x_2\) are given by:

\[ x_1^{(k)} = \gamma \left(\frac{\gamma - 1}{\gamma + 1}\right)^k, \quad x_2^{(k)} = \left(\frac{\gamma - 1}{\gamma + 1}\right)^k \]

Additional notes mention that the convergence is very slow if \(\gamma \gg 1\) or \(\gamma \ll 1\). An example is provided for \(\gamma = 10\).

The bottom part of the image features a plot with contour lines representing the quadratic function. The plot shows the trajectory of the optimization process, starting at \(x^{(0)}\) and moving towards the minimum. The axes are labeled \(x_1\) and \(x_2\), and the trajectory is marked with circles and lines.

**Source:**

A. d’Aspremont. Convex Optimization M2.
```
