Gradient #
Main Definitions #
Let f
be a function from a Hilbert Space F
to 𝕜
(𝕜
is ℝ
or ℂ
) , x
be a point in F
and f'
be a vector in F. Then
HasGradientWithinAt f f' s x
says that f
has a gradient f'
at x
, where the domain of interest
is restricted to s
. We also have
HasGradientAt f f' x := HasGradientWithinAt f f' x univ
Main results #
This file contains the following parts of gradient.
- the definition of gradient.
- the theorems translating between
HasGradientAtFilter
andHasFDerivAtFilter
,HasGradientWithinAt
andHasFDerivWithinAt
,HasGradientAt
andHasFDerivAt
,Gradient
andfderiv
. - theorems the Uniqueness of Gradient.
- the theorems translating between
HasGradientAtFilter
andHasDerivAtFilter
,HasGradientAt
andHasDerivAt
,Gradient
andderiv
whenF = 𝕜
. - the theorems about the congruence of the gradient.
- the theorems about the gradient of constant function.
- the theorems about the continuity of a function admitting a gradient.
A function f
has the gradient f'
as derivative along the filter L
if
f x' = f x + ⟨f', x' - x⟩ + o (x' - x)
when x'
converges along the filter L
.
Equations
- HasGradientAtFilter f f' x L = HasFDerivAtFilter f ((InnerProductSpace.toDual 𝕜 F) f') x L
Instances For
f
has the gradient f'
at the point x
within the subset s
if
f x' = f x + ⟨f', x' - x⟩ + o (x' - x)
where x'
converges to x
inside s
.
Equations
- HasGradientWithinAt f f' s x = HasGradientAtFilter f f' x (nhdsWithin x s)
Instances For
f
has the gradient f'
at the point x
if
f x' = f x + ⟨f', x' - x⟩ + o (x' - x)
where x'
converges to x
.
Equations
- HasGradientAt f f' x = HasGradientAtFilter f f' x (nhds x)
Instances For
Gradient of f
at the point x
within the set s
, if it exists. Zero otherwise.
If the derivative exists (i.e., ∃ f', HasGradientWithinAt f f' s x
), then
f x' = f x + ⟨f', x' - x⟩ + o (x' - x)
where x'
converges to x
inside s
.
Equations
- gradientWithin f s x = (LinearIsometryEquiv.symm (InnerProductSpace.toDual 𝕜 F)) (fderivWithin 𝕜 f s x)
Instances For
Gradient of f
at the point x
, if it exists. Zero otherwise.
If the derivative exists (i.e., ∃ f', HasGradientAt f f' x
), then
f x' = f x + ⟨f', x' - x⟩ + o (x' - x)
where x'
converges to x
.
Equations
- gradient f x = (LinearIsometryEquiv.symm (InnerProductSpace.toDual 𝕜 F)) (fderiv 𝕜 f x)
Instances For
Gradient of f
at the point x
, if it exists. Zero otherwise.
If the derivative exists (i.e., ∃ f', HasGradientAt f f' x
), then
f x' = f x + ⟨f', x' - x⟩ + o (x' - x)
where x'
converges to x
.
Equations
- Gradient.«term∇» = Lean.ParserDescr.node `Gradient.term∇ 1024 (Lean.ParserDescr.symbol "∇")
Instances For
Alias of the forward direction of hasGradientWithinAt_iff_hasFDerivWithinAt
.
Alias of the forward direction of hasFDerivWithinAt_iff_hasGradientWithinAt
.
Alias of the forward direction of hasGradientAt_iff_hasFDerivAt
.
Alias of the forward direction of hasFDerivAt_iff_hasGradientAt
.