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For example, if A is a 3-by-0 matrix and B is a 0-by-3 matrix, then AB is the 3-by-3 zero matrix corresponding to the null map from a 3-dimensional space V to itself, while BA is a 0-by-0 matrix. There is no common notation for empty matrices, but most computer algebra systems allow creating and computing with them.
Another policy commonly used by 4.0-scale schools is to mimic the eleven-point weighted scale (see below) by adding a .33 (one-third of a letter grade) to honors or advanced placement class. (For example, a B in a regular class would be a 3.0, but in honors or AP class it would become a B+, or 3.33).
In mathematics, the term "graded" has a number of meanings, mostly related: . In abstract algebra, it refers to a family of concepts: . An algebraic structure is said to be -graded for an index set if it has a gradation or grading, i.e. a decomposition into a direct sum = of structures; the elements of are said to be "homogeneous of degree i ".
The parallelogram defined by the rows of the above matrix is the one with vertices at (0, 0), (a, b), (a + c, b + d), and (c, d), as shown in the accompanying diagram. The absolute value of ad − bc is the area of the parallelogram, and thus represents the scale factor by which areas are transformed by A.
The polar decomposition factors a matrix into a pair, one of which is the unique closest orthogonal matrix to the given matrix, or one of the closest if the given matrix is singular. (Closeness can be measured by any matrix norm invariant under an orthogonal change of basis, such as the spectral norm or the Frobenius norm.)
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Grading in education is the application of standardized measurements to evaluate different levels of student achievement in a course. Grades can be expressed as letters (usually A to F), as a range (for example, 1 to 6), percentages, or as numbers out of a possible total (often out of 100).
One example is the squared Frobenius norm, which can be viewed as an -norm acting either entrywise, or on the singular values of the matrix: = ‖ ‖ = | | = =. In the multivariate case the effect of regularizing with the Frobenius norm is the same as the vector case; very complex models will have larger norms, and, thus, will be penalized ...