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  2. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The row space of this matrix is the vector space spanned by the row vectors. The column vectors of a matrix. The column space of this matrix is the vector space spanned by the column vectors. In linear algebra, the column space (also called the range or image) of a matrix A is the span (set of all possible linear combinations) of its column ...

  3. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Rank–nullity_theorem

    The rank–nullity theorem is a theorem in linear algebra, which asserts: the number of columns of a matrix M is the sum of the rank of M and the nullity of M ; and the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the image of f ) and the nullity of f (the dimension of the kernel of f ).

  4. Kernel (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(linear_algebra)

    The left null space of A is the same as the kernel of A T. The left null space of A is the orthogonal complement to the column space of A, and is dual to the cokernel of the associated linear transformation. The kernel, the row space, the column space, and the left null space of A are the four fundamental subspaces associated with the matrix A.

  5. Kernel (algebra) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(algebra)

    The kernel of a matrix, also called the null space, is the kernel of the linear map defined by the matrix. The kernel of a homomorphism is reduced to 0 (or 1) if and only if the homomorphism is injective, that is if the inverse image of every element consists of a single element. This means that the kernel can be viewed as a measure of the ...

  6. Rank (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Rank_(linear_algebra)

    Now, each row of A is given by a linear combination of the r rows of R. Therefore, the rows of R form a spanning set of the row space of A and, by the Steinitz exchange lemma, the row rank of A cannot exceed r. This proves that the row rank of A is less than or equal to the column rank of A.

  7. Sparse matrix - Wikipedia

    en.wikipedia.org/wiki/Sparse_matrix

    To extract the row 1 (the second row) of this matrix we set row_start=1 and row_end=2. Then we make the slices V[1:2] = [8] and COL_INDEX[1:2] = [1]. We now know that in row 1 we have one element at column 1 with value 8. In this case the CSR representation contains 13 entries, compared to 16 in the original matrix.

  8. Fredholm operator - Wikipedia

    en.wikipedia.org/wiki/Fredholm_operator

    In mathematics, Fredholm operators are certain operators that arise in the Fredholm theory of integral equations.They are named in honour of Erik Ivar Fredholm.By definition, a Fredholm operator is a bounded linear operator T : X → Y between two Banach spaces with finite-dimensional kernel ⁡ and finite-dimensional (algebraic) cokernel ⁡ = / ⁡, and with closed range ⁡.

  9. Column groups and row groups - Wikipedia

    en.wikipedia.org/wiki/Column_groups_and_row_groups

    Colored column groups and row groups in the periodic table of the chemical elements. In tables and matrices, a column group or row group usually refers to a subset of columns or rows, respectively. Short names or notational names include col group or colgroup, and row group or rowgroup. They can have varying uses depending on context: