Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variable projection methods for an optimized dynamic mode decomposition   The dynamic mode decomposition (DMD) has become a leading tool for
data-driven modeling of dynamical systems, providing a regression framework for
fitting linear dynamical models to time-series measurement data. We present a
simple algorithm for computing an optimized version of the DMD for data which
may be collected at unevenly spaced sample times. By making use of the variable
projection method for nonlinear least squares problems, the algorithm is
capable of solving the underlying nonlinear optimization problem efficiently.
We explore the performance of the algorithm with some numerical examples for
synthetic and real data from dynamical systems and find that the resulting
decomposition displays less bias in the presence of noise than standard DMD
algorithms. Because of the flexibility of the algorithm, we also present some
interesting new options for DMD-based analysis.

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The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph