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‘Naturally Occurring Radioactive Material’ (NORM) or ‘Low Specific Activity’ (LSA) are the collective names for these naturally occurring radioactive substances which may be released during the production of oil and natural gas.
Is Frobenius norm an induced norm?
induced norms are given in [7]. In this section we show that the Frobenius matrix norm is not induced. matrix norm, Theorem 3.1 specializes to Corollary 5.6.
What is LSA scale?
LSA scale (Low Specific Activity scale) is a radioactive deposit inside pipes and other production equipment and consists of carbonates and sulphates of Ca, Ba and co-precipitated Ra. • The salts were dissolved in the reservoir itself in a mixture of original formation water and injected seawater.
What is radioactive LSA?
Low Specific Activity (LSA) material means radioactive material with limited specific activity which is nonfissile or is excepted under § 71.15, and which satisfies the descriptions and limits set forth in the following section.
What does Frobenius norm do?
Frobenius norm . Recall that the trace function returns the sum of diagonal entries of a square matrix. and comes from the Frobenius inner product on the space of all matrices. The Frobenius norm is sub-multiplicative and is very useful for numerical linear algebra.
WHAT IS THE Frobenius norm used for?
Frobenius norm Recall that the trace function returns the sum of diagonal entries of a square matrix. and comes from the Frobenius inner product on the space of all matrices. The Frobenius norm is submultiplicative and is very useful for numerical linear algebra.
How is the SVD used to calculate Frobenius norm?
After proving the SVD theorem, the SVD is used to determine the four fundamental subspaces of a matrix and to develop formula for the Frobenius norm in terms of the singular values of a matrix. A geometric interpretation of the SVD is discussed, followed by a demonstration with a 2 × 2 matrix.
Which is the best definition of the Frobenius norm?
% Output: the Frobenius norm ∑ i = 1 m ∑ k = 1 n a ik 2. One of the oldest and simplest matrix norms is the Frobenius norm, sometimes called the Hilbert-Schmidt norm. It is defined as the square root of the sum of the squares of all the matrix entries, or | | A | | F = ∑ i = 1 m ∑ j = 1 n a ij 2 1 / 2.
How is the SVD theorem used in linear algebra?
This chapter covers the singular value decomposition (SVD), one of the greatest results in linear algebra. After proving the SVD theorem, the SVD is used to determine the four fundamental subspaces of a matrix and to develop formula for the Frobenius norm in terms of the singular values of a matrix.