
CCAMA provides a Matlab implementation of a customized alternating minimization algorithm for solving structured covariance completion problems. These problems aim to explain and complete partially available statistical signatures of dynamical systems by identifying the directionality and dynamics of input excitations into linear approximations of the system. In particular, we seek to explain correlation data with the least number of possible input disturbance channels. This inverse problem is formulated as a rank minimization, and for its solution, we employ a convex relaxation based on the nuclear norm. CCAMA exploits the structure of the formulated optimization problem in order to gain computational efficiency and improve scalability.
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