Higher differentiability of usual operations #
We prove that the usual operations (addition, multiplication, difference, and
so on) preserve C^n functions.
Notation #
We use the notation E [×n]→L[𝕜] F for the space of continuous multilinear maps on E^n with
values in F. This is the space in which the n-th derivative of a function from E to F lives.
In this file, we denote (⊤ : ℕ∞) : WithTop ℕ∞ with ∞ and ⊤ : WithTop ℕ∞ with ω.
Tags #
derivative, differentiability, higher derivative, C^n, multilinear, Taylor series, formal series
Smoothness of functions f : E → Π i, F' i #
Sum of two functions #
The sum of two C^n functions within a set at a point is C^n within this set
at this point.
The sum of two C^n functions at a point is C^n at this point.
The sum of two C^n functions is C^n.
The sum of two C^n functions on a domain is C^n.
The iterated derivative of the sum of two functions is the sum of the iterated derivatives.
Eta-expanded form of iteratedFDerivWithin_add_apply
The iterated derivative of the sum of two functions is the sum of the iterated derivatives.
Alias of fun_iteratedFDerivWithin_add_apply.
Eta-expanded form of iteratedFDerivWithin_add_apply
The iterated derivative of the sum of two functions is the sum of the iterated derivatives.
Eta-expanded form of iteratedFDeriv_add_apply
Alias of fun_iteratedFDeriv_add_apply.
Eta-expanded form of iteratedFDeriv_add_apply
Eta-expanded form of iteratedFDeriv_add
Negative #
The negative of a C^n function within a domain at a point is C^n within this domain at
this point.
The negative of a C^n function at a point is C^n at this point.
The negative of a C^n function is C^n.
The negative of a C^n function on a domain is C^n.
Subtraction #
The difference of two C^n functions within a set at a point is C^n within this set
at this point.
The difference of two C^n functions at a point is C^n at this point.
The difference of two C^n functions on a domain is C^n.
The difference of two C^n functions is C^n.
The iterated derivative of the difference of two functions is the difference of the iterated derivatives.
Eta-expanded form of iteratedFDerivWithin_sub_apply
The iterated derivative of the difference of two functions is the difference of the iterated derivatives.
The iterated derivative of the difference of two functions is the difference of the iterated derivatives.
Eta-expanded form of iteratedFDeriv_sub_apply
The iterated derivative of the difference of two functions is the difference of the iterated derivatives.
The iterated derivative of the difference of two functions is the difference of the iterated derivatives.
Eta-expanded form of iteratedFDeriv_sub
The iterated derivative of the difference of two functions is the difference of the iterated derivatives.
Sum of finitely many functions #
Product of two functions #
The product of two C^n functions within a set at a point is C^n within this set
at this point.
The product of two C^n functions at a point is C^n at this point.
The product of two C^n functions on a domain is C^n.
The product of two C^n functions is C^n.
Scalar multiplication #
The scalar multiplication of two C^n functions within a set at a point is C^n within this
set at this point.
Eta-expanded form of ContDiffWithinAt.smul
The scalar multiplication of two C^n functions within a set at a point is C^n within this
set at this point.
The scalar multiplication of two C^n functions at a point is C^n at this point.
Eta-expanded form of ContDiffAt.smul
The scalar multiplication of two C^n functions at a point is C^n at this point.
The scalar multiplication of two C^n functions is C^n.
Eta-expanded form of ContDiff.smul
The scalar multiplication of two C^n functions is C^n.
The scalar multiplication of two C^n functions on a domain is C^n.
Eta-expanded form of ContDiffOn.smul
The scalar multiplication of two C^n functions on a domain is C^n.
Constant scalar multiplication #
TODO: generalize results in this section -- if c is a unit (or R is a group), then one can
drop ContDiff* assumptions in some lemmas about iteratedFDeriv and iteratedFDerivWithin.
Scalar multiplication is smooth (as a function of the vector variable).
Scalar multiplication is smooth (as a function of the scalar variable).
The scalar multiplication of a constant and a C^n function within a set at a point is C^n
within this set at this point.
The scalar multiplication of C^n function within a set at a point and a constant and is C^n
within this set at this point.
The scalar multiplication of a constant and a C^n function at a point is C^n at this
point.
The scalar multiplication of a C^n function at a point and a constant is C^n at this
point.
The scalar multiplication of a constant and a C^n function is C^n.
The scalar multiplication of a C^n function and a constant is C^n.
The scalar multiplication of a constant and a C^n function on a domain is C^n.
The scalar multiplication of a C^n function on a domain and a constant is C^n.
Cartesian product of two functions #
The product map of two C^n functions within a set at a point is C^n
within the product set at the product point.
The product map of two C^n functions on a set is C^n on the product set.
The product map of two C^n functions within a set at a point is C^n
within the product set at the product point.
The product map of two C^n functions within a set at a point is C^n
within the product set at the product point.
The product map of two C^n functions is C^n.
Inversion in a complete normed algebra (or more generally with summable geometric series) #
In a complete normed algebra, the operation of inversion is C^n, for all n, at each
invertible element, as it is analytic.
Inversion of continuous linear maps between Banach spaces #
At a continuous linear equivalence e : E ≃L[𝕜] F between Banach spaces, the operation of
inversion is C^n, for all n.
At an invertible map e : M →L[R] M₂ between Banach spaces, the operation of
inversion is C^n, for all n.
If f is a local homeomorphism and the point a is in its target,
and if f is n times continuously differentiable at f.symm a,
and if the derivative at f.symm a is a continuous linear equivalence,
then f.symm is n times continuously differentiable at the point a.
This is one of the easy parts of the inverse function theorem: it assumes that we already have an inverse function.
If f is an n times continuously differentiable homeomorphism,
and if the derivative of f at each point is a continuous linear equivalence,
then f.symm is n times continuously differentiable.
This is one of the easy parts of the inverse function theorem: it assumes that we already have an inverse function.
Let f be a local homeomorphism of a nontrivially normed field, let a be a point in its
target. if f is n times continuously differentiable at f.symm a, and if the derivative at
f.symm a is nonzero, then f.symm is n times continuously differentiable at the point a.
This is one of the easy parts of the inverse function theorem: it assumes that we already have an inverse function.
Let f be an n times continuously differentiable homeomorphism of a nontrivially normed
field. Suppose that the derivative of f is never equal to zero. Then f.symm is n times
continuously differentiable.
This is one of the easy parts of the inverse function theorem: it assumes that we already have an inverse function.
Restrict an open partial homeomorphism to the subsets of the source and target
that consist of points x ∈ f.source, y = f x ∈ f.target
such that f is C^n at x and f.symm is C^n at y.
Note that n is a natural number or ω, but not ∞,
because the set of points of C^∞-smoothness of f is not guaranteed to be open.
Equations
Instances For
Restricting from ℂ to ℝ, or generally from 𝕜' to 𝕜 #
If a function is n times continuously differentiable over ℂ, then it is n times continuously
differentiable over ℝ. In this paragraph, we give variants of this statement, in the general
situation where ℂ and ℝ are replaced respectively by 𝕜' and 𝕜 where 𝕜' is a normed algebra
over 𝕜.