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Although the complexity of the aforementioned approaches based on matrix inversion can be further reduced if specific properties of the signal model are exploited [15, 22, 23], it still remains considerable.
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First, we review the recursive 4SID algorithm based on the matrix inversion lemma shown in our previous paper.
We show that, this being the case, a computational method based on a matrix inversion of the linearized constraint equations (MILC SHAKE) can be orders of magnitude faster than the simple SHAKE or RATTLE methods.
The LMS algorithm follows a stochastic gradient procedure, whereas the RLS algorithm is based on the matrix inversion lemma.
The fast implementation that we will now proceed to derive, is based on the matrix inversion lemma, and is basically a recursion over the model order.
However, the proposed SIC based iterative matrix inversion method has the least number of operations when allocating energies.
A new method is introduced based on the Sherman Morrison matrix inversion formula for calculation of the optimal absorber parameter values for attachment to a damped multi-degree of freedom system.
The precoding is based on rewriting the matrix inversion by a polynomial expansion, which is then truncated.
In our work, we develop and study successive optimization Tomlinson-Harashima precoding (SO-THP) algorithms based on the generalized matrix inversion approach reported in [11].
The less accurate methods based on sparse covariance matrix inversion such as PSICOV (Jones et al., 2012) or Mean Field Direct Coupling Analysis use the sequence information only in a preprocessing step, while the main computation in O(L) is independent of N.
This paper is concerned with scaling characteristics of Monte Carlo based algorithms for matrix inversion.
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