  The present study proposes a sensor selection method based on the proximal
splitting algorithm and the A-optimal design of experiment using the
alternating direction method of multipliers (ADMM) algorithm. The performance
of the proposed method was evaluated with a random sensor problem and compared
with the previously proposed methods such as the greedy method and the convex
relaxation. The performance of the proposed method is better than an existing
method in terms of the A-optimality criterion. In addition, the proposed method
requires longer computational time than the greedy method but it is quite
shorter than the convex relaxation in large-scale problems. The proposed method
was applied to the data-driven sparse-sensor-selection problem. A data set
adopted is the NOAA OISST V2 mean sea surface temperature set. At the number of
sensors larger than that of the latent state variables, the proposed method
showed similar and better performances compared with previously proposed
methods in terms of the A-optimality criterion and reconstruction error.
