Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight
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.
Code (2)
Tasks
Model DiscoveryregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
OKRidge: Scalable Optimal k-Sparse Ridge Regression
We consider an important problem in scientific discovery, namely identifying sparse governing equations for nonlinear dynamical systems. This involves solving sparse ridge regression problems to provable optimality in or…
regressionscientific discoverySparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions
We present a novel binary convex reformulation of the sparse regression problem that constitutes a new duality perspective. We devise a new cutting plane method and provide evidence that it can solve to provable optimali…
regressionVocal Bursts Intensity PredictionTabNSM: Neural Sparse Mixer for Tabular Regression
Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable flexible feature learning but often incur costly in…
Representation LearningIterative Hessian Sketch in Input Sparsity Time
Scalable algorithms to solve optimization and regression tasks even approximately, are needed to work with large datasets. In this paper we study efficient techniques from matrix sketching to solve a variety of convex co…
regressionDepth Completion using Plane-Residual Representation
The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data. In this paper, we try to solve this problem in a more effective way, by reformulating the regression-based …
Depth CompletionDepth EstimationDepth Predictionregression