Abstract
The field of algorithms covering implementations is very rich and versatile. In computer science, we ubiquitously use computational linear algebra in algorithms, often by using a dedicated software library that hides the tedious nitty-gritty details of the optimized implementations of the fundamental algorithms (mainly matrix arithmetic operations and factorization primitives).
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Notes
- 1.
Freely available online at http://www.scilab.org/.
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A matrix is said symmetric positive definite if and only if: \(\forall x\not =0, x^\top M x>0\). Positive definite matrices have all positive eigenvalues.
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By terminology, the precision matrix is the inverse of the covariance matrix.
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A matrix is Toeplitz if all its diagonals are constant.
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Nielsen, F. (2016). Parallel Linear Algebra. In: Introduction to HPC with MPI for Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-21903-5_5
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