How total derivative of a function works and what is the derivation of the formula?
If f is a function of $(x,y,z)$ then the total derivative is
$$d f = frac { partial f } { partial x } d x + frac { partial f } { partial y } d y + frac { partial f } { partial z } d z$$
but what is the derivation of the formula?
an user also explains the derivation in a similar question on this platform here
deriving the formula of total derivative of a multi-variable function
But the answer is too complex.Can someone explain this in a simpler way please?thanks .
calculus multivariable-calculus functions derivatives
add a comment |
If f is a function of $(x,y,z)$ then the total derivative is
$$d f = frac { partial f } { partial x } d x + frac { partial f } { partial y } d y + frac { partial f } { partial z } d z$$
but what is the derivation of the formula?
an user also explains the derivation in a similar question on this platform here
deriving the formula of total derivative of a multi-variable function
But the answer is too complex.Can someone explain this in a simpler way please?thanks .
calculus multivariable-calculus functions derivatives
Please see this to get a clearer understanding.
– Apoorv Khurasia
Dec 30 '18 at 7:16
add a comment |
If f is a function of $(x,y,z)$ then the total derivative is
$$d f = frac { partial f } { partial x } d x + frac { partial f } { partial y } d y + frac { partial f } { partial z } d z$$
but what is the derivation of the formula?
an user also explains the derivation in a similar question on this platform here
deriving the formula of total derivative of a multi-variable function
But the answer is too complex.Can someone explain this in a simpler way please?thanks .
calculus multivariable-calculus functions derivatives
If f is a function of $(x,y,z)$ then the total derivative is
$$d f = frac { partial f } { partial x } d x + frac { partial f } { partial y } d y + frac { partial f } { partial z } d z$$
but what is the derivation of the formula?
an user also explains the derivation in a similar question on this platform here
deriving the formula of total derivative of a multi-variable function
But the answer is too complex.Can someone explain this in a simpler way please?thanks .
calculus multivariable-calculus functions derivatives
calculus multivariable-calculus functions derivatives
edited Jan 2 at 12:02
Hawkingo
asked Dec 30 '18 at 5:03
HawkingoHawkingo
12
12
Please see this to get a clearer understanding.
– Apoorv Khurasia
Dec 30 '18 at 7:16
add a comment |
Please see this to get a clearer understanding.
– Apoorv Khurasia
Dec 30 '18 at 7:16
Please see this to get a clearer understanding.
– Apoorv Khurasia
Dec 30 '18 at 7:16
Please see this to get a clearer understanding.
– Apoorv Khurasia
Dec 30 '18 at 7:16
add a comment |
1 Answer
1
active
oldest
votes
I'm not sure whether you know what $df$ and the $dx_i$ are. Unless you arrive at the "official vue of things 2019" you cannot understand the formula in your question, let alone its proof. Hence, in the first place you have to have a good understanding of $df$ and the $dx_i$ as mathematical objects!
A differentiable function $$f:quad {bf R}^3to{mathbb R},qquad(x_1,x_2,x_3)mapsto f(x_1,x_2,x_3)$$ has at each point ${bf p}$ a derivative $df({bf p})$. This derivative is a linear function $$df({bf p}):quad T_{bf p}to{mathbb R},qquad{bf X}to df({bf p}).{bf X} ,$$ whereby ${bf X}=(X_1,X_2,X_3)$ is a variable ("small") increment (or tangent) vector attached at ${bf p}$. This function has the property that it linearly approximates function value increments $f({bf p}+{bf X})-f({bf p})$ as follows:
$$f({bf p}+{bf X})-f({bf p})=df({bf p}).{bf X}+o(|{bf X}|) qquad({bf X}to{bf 0}) .$$
Doing all computations etc. one arrives at the simple formula
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p})>X_1+{partial fover partial x_2}({bf p})>X_2+{partial fover partial x_3}({bf p})>X_3 .tag{1}$$
This formula shows how the linear map $df({bf p})$ is related to the partial derivatives (or to the Jacobian) of $f$. Now for the coordinate functions $x_i$ $(1leq ileq3)$ one has
$$x_i({bf p}+{bf X})-x_i({bf p})=X_i$$ without error term. It follows that $$dx_i.{bf X}=X_iqquad (1leq ileq 3)>,$$ so that we can rewrite $(1)$ as
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p}) dx_1.{bf X}+{partial fover partial x_2}({bf p}) dx_2.{bf X}+{partial fover partial x_3}({bf p}) dx_3.{bf X} .$$ Since this is true for all tangent vectors ${bf X}in T_{bf p}$ this shows that
$$df({bf p})={partial fover partial x_1}({bf p}),dx_1+{partial fover partial x_2}({bf p}),dx_2+{partial fover partial x_3}({bf p}),dx_3tag{2}$$
at every point ${bf p}$ in the domain of $f$. Omitting the ${bf p}$ the formula $(2)$ then can be abbreviated to
$$df={partial fover partial x_1} dx_1+{partial fover partial x_2} dx_2+{partial fover partial x_3} dx_3 .tag{3}$$
The formulas $(2)$, resp. $(3)$, say that at each point ${bf p}$ in the domain of $f$ the derivative $df({bf p})$ is a certain linear combination of the coordinate differentials $dx_i$, whereby the appearing coefficients are just the partial derivatives of $f$ at ${bf p}$.
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "69"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
noCode: true, onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f3056517%2fhow-total-derivative-of-a-function-works-and-what-is-the-derivation-of-the-formu%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I'm not sure whether you know what $df$ and the $dx_i$ are. Unless you arrive at the "official vue of things 2019" you cannot understand the formula in your question, let alone its proof. Hence, in the first place you have to have a good understanding of $df$ and the $dx_i$ as mathematical objects!
A differentiable function $$f:quad {bf R}^3to{mathbb R},qquad(x_1,x_2,x_3)mapsto f(x_1,x_2,x_3)$$ has at each point ${bf p}$ a derivative $df({bf p})$. This derivative is a linear function $$df({bf p}):quad T_{bf p}to{mathbb R},qquad{bf X}to df({bf p}).{bf X} ,$$ whereby ${bf X}=(X_1,X_2,X_3)$ is a variable ("small") increment (or tangent) vector attached at ${bf p}$. This function has the property that it linearly approximates function value increments $f({bf p}+{bf X})-f({bf p})$ as follows:
$$f({bf p}+{bf X})-f({bf p})=df({bf p}).{bf X}+o(|{bf X}|) qquad({bf X}to{bf 0}) .$$
Doing all computations etc. one arrives at the simple formula
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p})>X_1+{partial fover partial x_2}({bf p})>X_2+{partial fover partial x_3}({bf p})>X_3 .tag{1}$$
This formula shows how the linear map $df({bf p})$ is related to the partial derivatives (or to the Jacobian) of $f$. Now for the coordinate functions $x_i$ $(1leq ileq3)$ one has
$$x_i({bf p}+{bf X})-x_i({bf p})=X_i$$ without error term. It follows that $$dx_i.{bf X}=X_iqquad (1leq ileq 3)>,$$ so that we can rewrite $(1)$ as
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p}) dx_1.{bf X}+{partial fover partial x_2}({bf p}) dx_2.{bf X}+{partial fover partial x_3}({bf p}) dx_3.{bf X} .$$ Since this is true for all tangent vectors ${bf X}in T_{bf p}$ this shows that
$$df({bf p})={partial fover partial x_1}({bf p}),dx_1+{partial fover partial x_2}({bf p}),dx_2+{partial fover partial x_3}({bf p}),dx_3tag{2}$$
at every point ${bf p}$ in the domain of $f$. Omitting the ${bf p}$ the formula $(2)$ then can be abbreviated to
$$df={partial fover partial x_1} dx_1+{partial fover partial x_2} dx_2+{partial fover partial x_3} dx_3 .tag{3}$$
The formulas $(2)$, resp. $(3)$, say that at each point ${bf p}$ in the domain of $f$ the derivative $df({bf p})$ is a certain linear combination of the coordinate differentials $dx_i$, whereby the appearing coefficients are just the partial derivatives of $f$ at ${bf p}$.
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
add a comment |
I'm not sure whether you know what $df$ and the $dx_i$ are. Unless you arrive at the "official vue of things 2019" you cannot understand the formula in your question, let alone its proof. Hence, in the first place you have to have a good understanding of $df$ and the $dx_i$ as mathematical objects!
A differentiable function $$f:quad {bf R}^3to{mathbb R},qquad(x_1,x_2,x_3)mapsto f(x_1,x_2,x_3)$$ has at each point ${bf p}$ a derivative $df({bf p})$. This derivative is a linear function $$df({bf p}):quad T_{bf p}to{mathbb R},qquad{bf X}to df({bf p}).{bf X} ,$$ whereby ${bf X}=(X_1,X_2,X_3)$ is a variable ("small") increment (or tangent) vector attached at ${bf p}$. This function has the property that it linearly approximates function value increments $f({bf p}+{bf X})-f({bf p})$ as follows:
$$f({bf p}+{bf X})-f({bf p})=df({bf p}).{bf X}+o(|{bf X}|) qquad({bf X}to{bf 0}) .$$
Doing all computations etc. one arrives at the simple formula
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p})>X_1+{partial fover partial x_2}({bf p})>X_2+{partial fover partial x_3}({bf p})>X_3 .tag{1}$$
This formula shows how the linear map $df({bf p})$ is related to the partial derivatives (or to the Jacobian) of $f$. Now for the coordinate functions $x_i$ $(1leq ileq3)$ one has
$$x_i({bf p}+{bf X})-x_i({bf p})=X_i$$ without error term. It follows that $$dx_i.{bf X}=X_iqquad (1leq ileq 3)>,$$ so that we can rewrite $(1)$ as
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p}) dx_1.{bf X}+{partial fover partial x_2}({bf p}) dx_2.{bf X}+{partial fover partial x_3}({bf p}) dx_3.{bf X} .$$ Since this is true for all tangent vectors ${bf X}in T_{bf p}$ this shows that
$$df({bf p})={partial fover partial x_1}({bf p}),dx_1+{partial fover partial x_2}({bf p}),dx_2+{partial fover partial x_3}({bf p}),dx_3tag{2}$$
at every point ${bf p}$ in the domain of $f$. Omitting the ${bf p}$ the formula $(2)$ then can be abbreviated to
$$df={partial fover partial x_1} dx_1+{partial fover partial x_2} dx_2+{partial fover partial x_3} dx_3 .tag{3}$$
The formulas $(2)$, resp. $(3)$, say that at each point ${bf p}$ in the domain of $f$ the derivative $df({bf p})$ is a certain linear combination of the coordinate differentials $dx_i$, whereby the appearing coefficients are just the partial derivatives of $f$ at ${bf p}$.
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
add a comment |
I'm not sure whether you know what $df$ and the $dx_i$ are. Unless you arrive at the "official vue of things 2019" you cannot understand the formula in your question, let alone its proof. Hence, in the first place you have to have a good understanding of $df$ and the $dx_i$ as mathematical objects!
A differentiable function $$f:quad {bf R}^3to{mathbb R},qquad(x_1,x_2,x_3)mapsto f(x_1,x_2,x_3)$$ has at each point ${bf p}$ a derivative $df({bf p})$. This derivative is a linear function $$df({bf p}):quad T_{bf p}to{mathbb R},qquad{bf X}to df({bf p}).{bf X} ,$$ whereby ${bf X}=(X_1,X_2,X_3)$ is a variable ("small") increment (or tangent) vector attached at ${bf p}$. This function has the property that it linearly approximates function value increments $f({bf p}+{bf X})-f({bf p})$ as follows:
$$f({bf p}+{bf X})-f({bf p})=df({bf p}).{bf X}+o(|{bf X}|) qquad({bf X}to{bf 0}) .$$
Doing all computations etc. one arrives at the simple formula
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p})>X_1+{partial fover partial x_2}({bf p})>X_2+{partial fover partial x_3}({bf p})>X_3 .tag{1}$$
This formula shows how the linear map $df({bf p})$ is related to the partial derivatives (or to the Jacobian) of $f$. Now for the coordinate functions $x_i$ $(1leq ileq3)$ one has
$$x_i({bf p}+{bf X})-x_i({bf p})=X_i$$ without error term. It follows that $$dx_i.{bf X}=X_iqquad (1leq ileq 3)>,$$ so that we can rewrite $(1)$ as
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p}) dx_1.{bf X}+{partial fover partial x_2}({bf p}) dx_2.{bf X}+{partial fover partial x_3}({bf p}) dx_3.{bf X} .$$ Since this is true for all tangent vectors ${bf X}in T_{bf p}$ this shows that
$$df({bf p})={partial fover partial x_1}({bf p}),dx_1+{partial fover partial x_2}({bf p}),dx_2+{partial fover partial x_3}({bf p}),dx_3tag{2}$$
at every point ${bf p}$ in the domain of $f$. Omitting the ${bf p}$ the formula $(2)$ then can be abbreviated to
$$df={partial fover partial x_1} dx_1+{partial fover partial x_2} dx_2+{partial fover partial x_3} dx_3 .tag{3}$$
The formulas $(2)$, resp. $(3)$, say that at each point ${bf p}$ in the domain of $f$ the derivative $df({bf p})$ is a certain linear combination of the coordinate differentials $dx_i$, whereby the appearing coefficients are just the partial derivatives of $f$ at ${bf p}$.
I'm not sure whether you know what $df$ and the $dx_i$ are. Unless you arrive at the "official vue of things 2019" you cannot understand the formula in your question, let alone its proof. Hence, in the first place you have to have a good understanding of $df$ and the $dx_i$ as mathematical objects!
A differentiable function $$f:quad {bf R}^3to{mathbb R},qquad(x_1,x_2,x_3)mapsto f(x_1,x_2,x_3)$$ has at each point ${bf p}$ a derivative $df({bf p})$. This derivative is a linear function $$df({bf p}):quad T_{bf p}to{mathbb R},qquad{bf X}to df({bf p}).{bf X} ,$$ whereby ${bf X}=(X_1,X_2,X_3)$ is a variable ("small") increment (or tangent) vector attached at ${bf p}$. This function has the property that it linearly approximates function value increments $f({bf p}+{bf X})-f({bf p})$ as follows:
$$f({bf p}+{bf X})-f({bf p})=df({bf p}).{bf X}+o(|{bf X}|) qquad({bf X}to{bf 0}) .$$
Doing all computations etc. one arrives at the simple formula
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p})>X_1+{partial fover partial x_2}({bf p})>X_2+{partial fover partial x_3}({bf p})>X_3 .tag{1}$$
This formula shows how the linear map $df({bf p})$ is related to the partial derivatives (or to the Jacobian) of $f$. Now for the coordinate functions $x_i$ $(1leq ileq3)$ one has
$$x_i({bf p}+{bf X})-x_i({bf p})=X_i$$ without error term. It follows that $$dx_i.{bf X}=X_iqquad (1leq ileq 3)>,$$ so that we can rewrite $(1)$ as
$$df({bf p}).{bf X}={partial fover partial x_1}({bf p}) dx_1.{bf X}+{partial fover partial x_2}({bf p}) dx_2.{bf X}+{partial fover partial x_3}({bf p}) dx_3.{bf X} .$$ Since this is true for all tangent vectors ${bf X}in T_{bf p}$ this shows that
$$df({bf p})={partial fover partial x_1}({bf p}),dx_1+{partial fover partial x_2}({bf p}),dx_2+{partial fover partial x_3}({bf p}),dx_3tag{2}$$
at every point ${bf p}$ in the domain of $f$. Omitting the ${bf p}$ the formula $(2)$ then can be abbreviated to
$$df={partial fover partial x_1} dx_1+{partial fover partial x_2} dx_2+{partial fover partial x_3} dx_3 .tag{3}$$
The formulas $(2)$, resp. $(3)$, say that at each point ${bf p}$ in the domain of $f$ the derivative $df({bf p})$ is a certain linear combination of the coordinate differentials $dx_i$, whereby the appearing coefficients are just the partial derivatives of $f$ at ${bf p}$.
edited 11 hours ago
answered Jan 2 at 19:24
Christian BlatterChristian Blatter
172k7113326
172k7113326
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
add a comment |
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
sorry,I didn't understand your steps.Can you explain me a bit clearly.sorry my math is a little weak.
– Hawkingo
Jan 6 at 13:16
add a comment |
Thanks for contributing an answer to Mathematics Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f3056517%2fhow-total-derivative-of-a-function-works-and-what-is-the-derivation-of-the-formu%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Please see this to get a clearer understanding.
– Apoorv Khurasia
Dec 30 '18 at 7:16