The well established direct methods for updating, or inverting huge matrices fail due to the expense of a large increase in core-memory storage and CPU-time, even for moderately-sized systems.
Firstly, it is calculated only once per segment, and, secondly, many standard methods exist for updating the matrix inverse over time (see, e.g., [45]).
As an instance of a incorrigible failure in the homothetic testing, the GRAS method for updating the economic matrices with some negative entries is analyzed in details.
Computable error bounds for the updated matrices are also given by means of rigorous mathematical analysis.
Some well-known methods for matrix updating serve as an additional instrumental confirmation to validity of homothetic paradigm.
In particular, as it is shown below, the well-known and widely used RAS and Kuroda's methods for matrix updating serve as an additional instrumental confirmation to such an answer.
Since the channel matrix H ω is not available, we resort to a simple stochastic gradient method for updating the beamformers.
Firstly, the communication updating matrix W updates according to the participating agents updating method described in (6) to adapt to the plug-in operation.
Current methods for model updating often use discrete parameters, such as thickness or joint stiffness, for model updating.
We use the same method as [34] for updating D h and D v and choose the proper Lipschitz matrix for updating x.
In this article, we have considered four methods of matrix updating that can be used for handling the matrices with some negative entries, namely, Kuroda's, iWSD, iWSRD and GRAS (including mGRAS) methods.
