  Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a
system's dynamics that is built from data. We seek to reduce the order of this
model by identifying a reduced set of modes that best fit the output. We adopt
a model selection algorithm from statistics and machine learning known as Least
Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS
to select DMD modes. We refer to the resulting algorithm as Least Angle
Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting
Dynamic Mode Decomposition (DMDSP), a popular mode-selection algorithm, serves
as a benchmark for comparison. Numerical results from a Poiseuille flow test
problem show that LARS4DMD yields reduced-order models that have comparable
performance to DMDSP. LARS4DMD has the added benefit that the regularization
weighting parameter required for DMDSP is not needed.
