  This paper proposes a convex approach to the Frisch-Kalman problem that
identifies the linear relations among variables from noisy observations. The
problem was proposed by Ragnar Frisch in 1930s, and was promoted and further
developed by Rudolf Kalman later in 1980s. It is essentially a rank
minimization problem with convex constraints. Regarding this problem,
analytical results and heuristic methods have been pursued over a half century.
The proposed convex method in this paper is shown to be accurate and
demonstrated to outperform several commonly adopted heuristics when the noise
components are relatively small compared with the underlying data.
