Low-Rank Dynamic Mode Decomposition: An Exact and Tractable Solution
This provides an exact solution for a known bottleneck in data-driven dynamical systems modeling, which is incremental over prior sub-optimal methods.
The paper tackles the non-convex optimization problem in low-rank dynamic mode decomposition for approximating high-dimensional dynamical systems, showing that an exact closed-form solution exists and can be computed in polynomial time, with algorithms evaluated on synthetic and physical benchmarks.
This work studies the linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD). Searching this approximation in a data-driven approach is formalised as attempting to solve a low-rank constrained optimisation problem. This problem is non-convex and state-of-the-art algorithms are all sub-optimal. This paper shows that there exists a closed-form solution, which is computed in polynomial time, and characterises the l2-norm of the optimal approximation error. The paper also proposes low-complexity algorithms building reduced models from this optimal solution, based on singular value decomposition or eigen value decomposition. The algorithms are evaluated by numerical simulations using synthetic and physical data benchmarks.