Low-Rank Dynamic Mode Decomposition: An Exact and Tractable Solution
For researchers in dynamical systems and data-driven modeling, this work offers an exact and tractable solution to a previously sub-optimally solved problem, enabling more accurate low-rank approximations.
This paper provides a closed-form, polynomial-time solution to the low-rank constrained optimization problem in dynamic mode decomposition, which was previously non-convex and lacked optimal algorithms. The solution characterizes the optimal approximation error in l2-norm and is validated 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.