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  2. Determinant - Wikipedia

    en.wikipedia.org/wiki/Determinant

    This key fact can be proven by observing that, for a fixed matrix , both sides of the equation are alternating and multilinear as a function depending on the columns of . Moreover, they both take the value det B {\displaystyle \det B} when A {\displaystyle A} is the identity matrix.

  3. Jacobi's formula - Wikipedia

    en.wikipedia.org/wiki/Jacobi's_formula

    In matrix calculus, Jacobi's formula expresses the derivative of the determinant of a matrix A in terms of the adjugate of A and the derivative of A. [ 1 ] If A is a differentiable map from the real numbers to n × n matrices, then

  4. Jacobian matrix and determinant - Wikipedia

    en.wikipedia.org/wiki/Jacobian_matrix_and...

    The Jacobian matrix represents the differential of f at every point where f is differentiable. In detail, if h is a displacement vector represented by a column matrix, the matrix product J(x) ⋅ h is another displacement vector, that is the best linear approximation of the change of f in a neighborhood of x, if f(x) is differentiable at x.

  5. Rule of Sarrus - Wikipedia

    en.wikipedia.org/wiki/Rule_of_Sarrus

    Rule of Sarrus: The determinant of the three columns on the left is the sum of the products along the down-right diagonals minus the sum of the products along the up-right diagonals. In matrix theory , the rule of Sarrus is a mnemonic device for computing the determinant of a 3 × 3 {\displaystyle 3\times 3} matrix named after the French ...

  6. Leibniz formula for determinants - Wikipedia

    en.wikipedia.org/wiki/Leibniz_formula_for...

    In algebra, the Leibniz formula, named in honor of Gottfried Leibniz, expresses the determinant of a square matrix in terms of permutations of the matrix elements. If A {\displaystyle A} is an n × n {\displaystyle n\times n} matrix, where a i j {\displaystyle a_{ij}} is the entry in the i {\displaystyle i} -th row and j {\displaystyle j} -th ...

  7. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    In matrix inversion however, instead of vector b, we have matrix B, where B is an n-by-p matrix, so that we are trying to find a matrix X (also a n-by-p matrix): = =. We can use the same algorithm presented earlier to solve for each column of matrix X. Now suppose that B is the identity matrix of size n.

  8. Cauchy–Binet formula - Wikipedia

    en.wikipedia.org/wiki/Cauchy–Binet_formula

    If n = m, the case where A and B are square matrices, ([]) = {[]} (a singleton set), so the sum only involves S = [n], and the formula states that det(AB) = det(A)det(B). For m = 0, A and B are empty matrices (but of different shapes if n > 0), as is their product AB ; the summation involves a single term S = Ø, and the formula states 1 = 1 ...

  9. Cramer's rule - Wikipedia

    en.wikipedia.org/wiki/Cramer's_rule

    where adj(A) denotes the adjugate matrix, det(A) is the determinant, and I is the identity matrix. If det(A) is nonzero, then the inverse matrix of A is = ⁡ (). This gives a formula for the inverse of A, provided det(A) ≠ 0. In fact, this formula works whenever F is a commutative ring, provided that det(A) is a unit.