  In this letter, we propose a simple yet effective singular value
decomposition (SVD) based strategy to reduce the optimization problem dimension
in data-enabled predictive control (DeePC). Specifically, in the case of linear
time-invariant systems, the excessive input/output measurements can be
rearranged into a smaller data library for the non-parametric representation of
system behavior. Based on this observation, we develop an SVD-based strategy to
pre-process the offline data that achieves dimension reduction in DeePC.
Numerical experiments confirm that the proposed method significantly enhances
the computation efficiency without sacrificing the control performance.
