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  2. Jacobian matrix and determinant - Wikipedia

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

    In vector calculus, the Jacobian matrix (/ dʒ ə ˈ k oʊ b i ə n /, [1] [2] [3] / dʒ ɪ-, j ɪ-/) of a vector-valued function of several variables is the matrix of all its first-order partial derivatives.

  3. Inverse function theorem - Wikipedia

    en.wikipedia.org/wiki/Inverse_function_theorem

    For functions of more than one variable, the theorem states that if is a continuously differentiable function from an open subset of into , and the derivative ′ is invertible at a point a (that is, the determinant of the Jacobian matrix of f at a is non-zero), then there exist neighborhoods of in and of = such that () and : is bijective. [1]

  4. Jacobian conjecture - Wikipedia

    en.wikipedia.org/wiki/Jacobian_conjecture

    The polynomial xx p has derivative 1 − p x p−1 which is 1 (because px is 0) but it has no inverse function. However, Kossivi Adjamagbo suggested extending the Jacobian conjecture to characteristic p > 0 by adding the hypothesis that p does not divide the degree of the field extension k(X) / k(F). [3]

  5. Broyden's method - Wikipedia

    en.wikipedia.org/wiki/Broyden's_method

    [1] Newton's method for solving f(x) = 0 uses the Jacobian matrix, J, at every iteration. However, computing this Jacobian can be a difficult and expensive operation; for large problems such as those involving solving the Kohn–Sham equations in quantum mechanics the number of variables can be in the hundreds of thousands. The idea behind ...

  6. Quasi-Newton method - Wikipedia

    en.wikipedia.org/wiki/Quasi-Newton_method

    The main difference is that the Hessian matrix is a symmetric matrix, unlike the Jacobian when searching for zeroes. Most quasi-Newton methods used in optimization exploit this symmetry. In optimization, quasi-Newton methods (a special case of variable-metric methods) are algorithms for finding local maxima and minima of functions.

  7. Gauss–Newton algorithm - Wikipedia

    en.wikipedia.org/wiki/Gauss–Newton_algorithm

    The Gauss-Newton iteration is guaranteed to converge toward a local minimum point ^ under 4 conditions: [4] The functions , …, are twice continuously differentiable in an open convex set ^, the Jacobian (^) is of full column rank, the initial iterate () is near ^, and the local minimum value | (^) | is small.

  8. Numerical continuation - Wikipedia

    en.wikipedia.org/wiki/Numerical_continuation

    The same terminology applies. A regular solution is a solution at which the Jacobian is full rank (). A singular solution is a solution at which the Jacobian is less than full rank. A regular solution lies on a k-dimensional surface, which can be parameterized by a point in the tangent space (the null space of the Jacobian).

  9. Jacobian variety - Wikipedia

    en.wikipedia.org/wiki/Jacobian_variety

    In mathematics, the Jacobian variety J(C) of a non-singular algebraic curve C of genus g is the moduli space of degree 0 line bundles. It is the connected component of the identity in the Picard group of C , hence an abelian variety .