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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.
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]
The polynomial x − x 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]
[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 ...
A matrix whose entries are all either 0 or 1. Synonym for (0,1)-matrix or logical matrix. [1] Bisymmetric matrix: A square matrix that is symmetric with respect to its main diagonal and its main cross-diagonal. Block-diagonal matrix: A block matrix with entries only on the diagonal. Block matrix: A matrix partitioned in sub-matrices called blocks.
The SR1 formula does not guarantee the update matrix to maintain positive-definiteness and can be used for indefinite problems. The Broyden's method does not require the update matrix to be symmetric and is used to find the root of a general system of equations (rather than the gradient) by updating the Jacobian (rather than the Hessian).
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.
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).