  There exist endless examples of dynamical systems with vast available data
and unsatisfying mathematical descriptions. Sparse regression applied to
symbolic libraries has quickly emerged as a powerful tool for learning
governing equations directly from data; these learned equations balance
quantitative accuracy with qualitative simplicity and human interpretability.
Here, I present a general purpose, model agnostic sparse regression algorithm
that extends a recently proposed exhaustive search leveraging iterative
Singular Value Decompositions (SVD). This accelerated scheme, Scalable Pruning
for Rapid Identification of Null vecTors (SPRINT), uses bisection with analytic
bounds to quickly identify optimal rank-1 modifications to null vectors. It is
intended to maintain sensitivity to small coefficients and be of reasonable
computational cost for large symbolic libraries. A calculation that would take
the age of the universe with an exhaustive search but can be achieved in a day
with SPRINT.
