  Rescaling a vector $\vec{\delta} \in \mathbb{R}^n$ to a desired length is a
common operation in many areas such as data science and machine learning. When
the rescaled perturbation $\eta \vec{\delta}$ is added to a starting point
$\vec{x} \in D$ (where $D$ is the data domain, e.g. $D = [0, 1]^n$), the
resulting vector $\vec{v} = \vec{x} + \eta \vec{\delta}$ will in general not be
in $D$. To enforce that the perturbed vector $v$ is in $D$, the values of
$\vec{v}$ can be clipped to $D$. This subsequent element-wise clipping to the
data domain does however reduce the effective perturbation size and thus
interferes with the rescaling of $\vec{\delta}$. The optimal rescaling $\eta$
to obtain a perturbation with the desired norm after the clipping can be
iteratively approximated using a binary search. However, such an iterative
approach is slow and non-differentiable. Here we show that the optimal
rescaling can be found analytically using a fast and differentiable algorithm.
Our algorithm works for any p-norm and can be used to train neural networks on
inputs with normalized perturbations. We provide native implementations for
PyTorch, TensorFlow, JAX, and NumPy based on EagerPy.

Machine Learning;Machine Learning