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In [12, 13], two fast implementations of IAA have been proposed by using the matrix computation technique such as Gohberg-Semencul decomposition, etc.
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With the techniques of vectorization and multi-dimensional matrix computation, the TIMMA-R algorithm has been made more efficient and scalable for large-scale analyzes.
PCA computation consists of four stages [21, 37, 38]: mean computation, covariance matrix computation, eigenvalue matrix, thus eigenvector computation, and PCs matrix computation.
After investigating various techniques to perform EVD (presented in Section 2.2.1), we selected the QR algorithm [29] for our eigenvalue matrix computation.
Orthogonalization is studied as a prototypical matrix computation task.
Eigenvalue matrix computation can be illustrated using the two Eqs.
We describe an alternative computation technique based on automatic differentiation.
The control scheme uses PID controller with evolutionary computation technique.
The three packages handle matrix computation in different ways.
These include alignment of reads, count matrix computation, and normalization.
Section 3.3 presents weight-by-weight modification of L-M that accelerates real-time computation by avoiding inverse matrix computation.
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