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  2. Cramer's rule - Wikipedia

    en.wikipedia.org/wiki/Cramer's_rule

    In linear algebra, Cramer's rule is an explicit formula for the solution of a system of linear equations with as many equations as unknowns, valid whenever the system has a unique solution. It expresses the solution in terms of the determinants of the (square) coefficient matrix and of matrices obtained from it by replacing one column by the ...

  3. Unimodular matrix - Wikipedia

    en.wikipedia.org/wiki/Unimodular_matrix

    In mathematics, a unimodular matrix M is a square integer matrix having determinant +1 or −1. Equivalently, it is an integer matrix that is invertible over the integers: there is an integer matrix N that is its inverse (these are equivalent under Cramer's rule).

  4. System of linear equations - Wikipedia

    en.wikipedia.org/wiki/System_of_linear_equations

    5.4 Cramer's rule. 5.5 Matrix solution. 5.6 Other methods. 6 Homogeneous systems. ... Repeat steps 1 and 2 until the system is reduced to a single linear equation.

  5. Rouché–Capelli theorem - Wikipedia

    en.wikipedia.org/wiki/Rouché–Capelli_theorem

    Consider the system of equations x + y + 2z = 3, x + y + z = 1, 2x + 2y + 2z = 2.. The coefficient matrix is = [], and the augmented matrix is (|) = [].Since both of these have the same rank, namely 2, there exists at least one solution; and since their rank is less than the number of unknowns, the latter being 3, there are infinitely many solutions.

  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. Matrix (mathematics) - Wikipedia

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

    Determinants can be used to solve linear systems using Cramer's rule, where the division of the determinants of two related square matrices equates to the value of each of the system's variables. [38]

  8. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    For example, to solve a system of n equations for n unknowns by performing row operations on the matrix until it is in echelon form, and then solving for each unknown in reverse order, requires n(n + 1)/2 divisions, (2n 3 + 3n 2 − 5n)/6 multiplications, and (2n 3 + 3n 2 − 5n)/6 subtractions, [10] for a total of approximately 2n 3 /3 operations.

  9. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    [5] Decomposition: A = Q S {\displaystyle A=QS} , where Q is a complex orthogonal matrix and S is complex symmetric matrix. Uniqueness: If A T A {\displaystyle A^{\mathsf {T}}A} has no negative real eigenvalues, then the decomposition is unique.