```markdown
The image is a slide from a presentation on convex optimization. It describes an optimization problem and associated force fields. The problem is to minimize \( c^T x \) subject to \( a_i^T x \leq b_i \) for \( i = 1, \ldots, m \).

- **Objective Force Field**: Constant, denoted as \( F_0(x) = -tc \).
- **Constraint Force Field**: Decays as the inverse distance to the constraint hyperplane, described by:
  \[
  F_i(x) = \frac{-a_i}{b_i - a_i^T x}, \quad \|F_i(x)\|_2 = \frac{1}{\text{dist}(x, \mathcal{H}_i)}
  \]
  where \( \mathcal{H}_i = \{ x \mid a_i^T x = b_i \} \).

The slide includes two diagrams illustrating the force field at different times (\( t = 1 \) and \( t = 3 \)). The vectors are shown originating from a point, with labels \(-c\) and \(-3c\), indicating the direction and magnitude of the force field at those times.

The slide is authored by A. d'Aspremont and is part of "Convex Optimization M2," slide number 12 out of 36.
```