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Theoretically, our method significantly improves upon the existing literature by eliminating the structural assumptions on the input matrices.
The parameter uncertainties are assumed to be time-varying norm-bounded appearing in both state and input matrices.
We provide an LMI solution that does not require invertibility of the input matrices of each subsystem.
The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices.
Most of the existing methods and theory in the context of specific statistical models that can be recast into sparse GEP require restrictive structural assumptions on the input matrices.
We relax the streaming model's constraint on input reuse and perform an in-depth analysis of dense matrix-matrix multiplication, which reuses each element of input matrices O(n) times.
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Check for input matrix format and size.
And this is going to be my input matrix.
Based on the control input matrix and output matrix, we decompose the system into two subsystems.
In addition, the lifted models provide avenues for interesting extensions such as network topology optimization, input matrix completion, and robustness against noisy inputs.
* n is the number of columns of A (and u) and the order of v. * u contains the rectangular input matrix A to be decomposed.
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