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To assess the correlations between the matrices, the linear regression and Pearson's correlations were calculated.
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The singular value decomposition is used to obtain the coefficient matrices, while the linear matrix inequality methodology is used to obtain the parameter-dependent state feedback gain of the control scheme.
Given an estimate of the coupling matrix, the linear superposition of external modes that produces a desired internal target field can be determined.
As N is the related noise matrix, the linear spectral mixing model (LSMM) can be written as R = A S + N = X + N (1).
At the coarsest level, as the coefficient matrix of the discrete system is a symmetric and positive definite M-matrix, the linear equations are solved with a symmetric successive overrelaxation preconditioned conjugate gradient method).
The coefficient matrices of the linear systems are structured such that their matrix-vector multiplications can be computed efficiently.
The problems were resolved with sparse matrices and the linear programming algorithm.
When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator.
then the matrices of the linear operators,andnd in the pullback bases are.
In this case, diagonal blocks are tridiagonal matrices and the linear system solution has a complexity of O(n).
In this case, the resulting matrix is an M-matrix, thus the linear system is invertible.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com