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2. Matrix (mathematics) - Wikipedia

   en.wikipedia.org/wiki/Matrix_(mathematics)

   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.

3. Array (data structure) - Wikipedia

   en.wikipedia.org/wiki/Array_(data_structure)

   For example: int a[2][3]; This means that array a has 2 rows and 3 columns, and the array is of integer type. Here we can store 6 elements they will be stored linearly but starting from first row linear then continuing with second row. The above array will be stored as a 11, a 12, a 13, a 21, a 22, a 23.

4. Index notation - Wikipedia

   en.wikipedia.org/wiki/Index_notation

   A vector treated as an array of numbers by writing as a row vector or column vector (whichever is used depends on convenience or context): = (), = Index notation allows indication of the elements of the array by simply writing a i, where the index i is known to run from 1 to n, because of n-dimensions. [1]

5. Third grade - Wikipedia

   en.wikipedia.org/wiki/Third_grade

   In mathematics, students are usually introduced to multiplication and division facts, place value to thousands or ten thousands, and estimation.Depending on the elementary school, third grade students may even begin to work on long division, such as dividings in the double digits, hundreds, and thousands.

6. Determinant - Wikipedia

   en.wikipedia.org/wiki/Determinant

   In mathematics, the determinant is a scalar-valued function of the entries of a square matrix.The determinant of a matrix A is commonly denoted det(A), det A, or | A |.Its value characterizes some properties of the matrix and the linear map represented, on a given basis, by the matrix.

7. Kronecker product - Wikipedia

   en.wikipedia.org/wiki/Kronecker_product

   In mathematics, the Kronecker product, sometimes denoted by ⊗, is an operation on two matrices of arbitrary size resulting in a block matrix.It is a specialization of the tensor product (which is denoted by the same symbol) from vectors to matrices and gives the matrix of the tensor product linear map with respect to a standard choice of basis.