Multivariable chain-rule proof?

In summary, The multivariable chain rule can be found in Spivak's "Calculus on Manifolds" and can also be proven using the limit definition of derivatives for g◦f, where the linear function in the limit is dg(f(x))df(x). There are also more rigorous proofs available that use linear mappings to show that derivatives are also linear mappings.
  • #1
ice109
1,714
6
can someone link/show me a formal proof of the multivariable chain rule?
 
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  • #2
There's a cute proof of this in Spivak's "Calculus on Manifolds". If you want to try it yourself, try defining the function fd(x) = f(x + h) - f(x) - df(h) and similarly for g, pretty much the numerator in the definition of the derivative, then evaluating the limit in the definition of the derivative for [itex]g\circ f[/itex], where you let the linear function in the limit be dg(f(x))df(x).
 
  • #3
what? how is this multavariable?
 
  • #4
ice109 said:
what? how is this multavariable?

let x be a vector x=(x1,x2,...,xn)
 
  • #5
There must be a better proof of the chain rule that that.
 
  • #6
ObsessiveMathsFreak said:
There must be a better proof of the chain rule than that.

yea i thought of that and thought the same thing; that there's got to be a more rigorous proof.
 
  • #7
ice109 said:
yea i thought of that and thought the same thing; that there's got to be a more rigorous proof.

more rigorous than very rigorous
interesting
maybe you mean more detailed
that I can provide

Chain rule
let
f:E->F
g:F->G
with E (or a subset) open in F
F (or a subset) open in G
and f differentiable at x
g differentiable at f(x)
then
(g◦f)' exist with
(g◦f)'=[g'◦f][f']
or in more full notation
[g(f(x))]'=[g'(f(x)][f'(x)]
notes on mappings
f:E->F
g:F->G
f':E->L(E,F)
g':F->L(F,G)
(g◦f):E->G
(g◦f)':E->L(E,G)
g'◦f:E->(F,G)
[g'◦f][f']:E->L(E,G)
where L(E,F) is a space of linear mappings from E to F
so all is as it should be
thus derivatives are linear mappings
Δx=dx
Δf:=f(x+dx)-f(x)
df:=f'(x)dx
Δf=df+o(dx) (f differentiable)
informal derivation
dg(f(x))=g'(f(x))df(x)+o(df(x))=g'(f(x))f'(x)dx+o(dx)+f'(x)o(dx)
more formal
let
Δf=df+|dx|rf
rf=(Δf-df)/|dx|
so lim rf=0
now
lim rf=lim rg=0 (f,g differentiable)
Δ[g(f(x))]=g'(f(x))Δf(x)+|Δf|rg(f(x))
=g'(f(x))f'(x)dx+|dx|r(f)+|dx||f'(x)dx/|dx|+rf|rg)
=g'(f(x))f'(x)dx+|dx|{rf+|f'(x)dx/|dx|+rf|rg}
we now need only
lim {r(f)+|f'(x)dx/|dx|+rf(x)|rg(f(x))}=0
which is clear from
rf(x)->0
rg(f(x))->0
and
|f'(x)dx/|dx|+rf|<|f'(x)|+|rf|<∞
ie bounded near x
where |f'(x)| is the norm induced on linear maps by the norm on vectors
 
Last edited:

1. What is the Multivariable Chain Rule?

The Multivariable Chain Rule is a mathematical rule used to find the derivative of a function with multiple variables. It is based on the concept of composition of functions, where one function is nested within another.

2. How is the Multivariable Chain Rule proven?

The Multivariable Chain Rule is proven using the limit definition of a derivative. This involves taking the limit of a difference quotient as the variables approach a specific value. By applying this limit to each variable in the function, the Multivariable Chain Rule can be derived.

3. Why is the Multivariable Chain Rule important?

The Multivariable Chain Rule is important because it allows us to find the rate of change of a function with multiple variables. This is essential in many fields of science and engineering, such as physics, economics, and engineering.

4. What are the key steps in proving the Multivariable Chain Rule?

The key steps in proving the Multivariable Chain Rule include: identifying the function and its variables, taking the partial derivatives of the function, applying the limit definition of a derivative to each variable, and simplifying the resulting expression.

5. Are there any applications of the Multivariable Chain Rule?

Yes, there are many applications of the Multivariable Chain Rule in various fields of science and engineering. It is used in physics to calculate the acceleration of a moving object, in economics to determine the rate of change of supply and demand, and in engineering to optimize complex systems.

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