# Optimal low-rank Dynamic Mode Decomposition

@article{Has2017OptimalLD, title={Optimal low-rank Dynamic Mode Decomposition}, author={P. H{\'e}as and C. Herzet}, journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2017}, pages={4456-4460} }

Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extended DMD to the context of low-rank approximations. This extension is of particular interest for reduced-order modeling in various applicative domains, e.g., for climate prediction, to study molecular dynamics or microelectromechanical devices. This low-rank extension takes the form of a non-convex optimization problem… CONTINUE READING

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#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 20 REFERENCES

Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses

- Computer Science, Mathematics
- 2012

380

A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

- Mathematics, Computer Science
- 2015

470

An overview of approximation methods for large-scale dynamical systems

- Mathematics, Computer Science
- 2005

156

Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

- Mathematics, Computer Science
- 2010

2858