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.

通过稀疏回归算法，特别是利用迭代奇异值分解的穷尽搜索，提出了一种通用的快速稀疏回归算法（SPRINT），用于从数据中直接学习具备定性简单性和人类可解释性的数学描述，该算法在大型符号库上具有合理的计算成本。