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・ Inverse copular constructions
・ Inverse Cost and Quality Law
・ Inverse curve
・ Inverse demand function
・ Inverse distance weighting
・ Inverse distribution
・ Inverse dynamics
・ Inverse electron-demand Diels–Alder reaction
・ Inverse element
・ Inverse exchange-traded fund
・ Inverse Faraday effect
・ Inverse filter
・ Inverse filter (disambiguation)
・ Inverse floating rate note
・ Inverse function
Inverse function theorem
・ Inverse functions and differentiation
・ Inverse Galois problem
・ Inverse gambler's fallacy
・ Inverse gas chromatography
・ Inverse Gaussian distribution
・ Inverse hyperbolic function
・ Inverse image functor
・ Inverse iteration
・ Inverse kinematics
・ Inverse Laplace transform
・ Inverse limit
・ Inverse magnetostrictive effect
・ Inverse mapping theorem
・ Inverse matrix gamma distribution


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Inverse function theorem : ウィキペディア英語版
Inverse function theorem
In mathematics, specifically differential calculus, the inverse function theorem gives sufficient conditions for a function to be invertible in a neighborhood of a point in its domain. The theorem also gives a formula for the derivative of the inverse function.
In multivariable calculus, this theorem can be generalized to any continuously differentiable, vector-valued function whose Jacobian determinant is nonzero at a point in its domain. In this case, the theorem gives a formula for the Jacobian matrix of the inverse. There are also versions of the inverse function theorem for complex holomorphic functions, for differentiable maps between manifolds, for differentiable functions between Banach spaces, and so forth.
==Statement of the theorem==
For functions of a single variable, the theorem states that if f is a continuously differentiable function with nonzero derivative at the point a, then f is invertible in a neighborhood of a, the inverse is continuously differentiable, and
:\bigl(f^\bigr)'(f(a)) = \frac,
where notationally the left side refers to the derivative of the inverse function evaluated at its value ''f''(''a''). For functions of more than one variable, the theorem states that if the total derivative of a continuously differentiable function F defined from an open set of \mathbb^n into \mathbb^n is invertible at a point p (i.e., the Jacobian determinant of F at p is non-zero), then F is an invertible function near p. That is, an inverse function to F exists in some neighborhood of F(p). Moreover, the inverse function F^ is also continuously differentiable. In the infinite dimensional case it is required that the Fréchet derivative have a bounded inverse at p.
Finally, the theorem says that
: J_,
where ()^ denotes matrix inverse and J_F(p) is the Jacobian matrix of the function F at
the point p.
This formula can also be derived from the chain rule. The chain rule states that for functions G and H which have total derivatives at H(p) and p respectively,
:J_ (p) = J_G (H(p)) \cdot J_H (p).
Letting G be F^ and H be F, G \circ H is the identity function, whose Jacobian matrix is also
the identity. In this special case, the formula above can be solved for J_ has a total derivative at p.
The existence of an inverse function to F is equivalent to saying that the system of n equations y_i = F_i(x_1, \dots, x_n) can be solved for x_1, \dots, x_n in terms of y_1, \dots, y_n if we restrict x and y to small enough neighborhoods of p and F(p), respectively.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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