Direction vectors are linearly dependent, if $D_vf(x)=D_wf(x)=0$ with $operatornamegrad f(x)neq 0$

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Let $f colon mathbbR^2 to mathbbR$ be continuous differentiable and $x inmathbbR^2$ with $operatornamegrad f(x) neq 0$. Let $v,w in mathbbR^2$ be direction vectors.



If $D_v f(x) = D_w f(x)=0$ then are $v$ and $w$ linearly dependent.




For my proof I use the following theorem:




Let $U subset mathbbR^m$ be open and $f colon U to mathbbR$ continuous differentiable. Then it holds for $x in U$ and $v in mathbbR^m$ with $|v| = 1$ that $D_v f(x) = langle v, operatornamegrad f(x) rangle$.




Proof:



With the theorem above and the assumption, we know that
$$
langle v, operatornamegrad f(x) rangle
= langle w, operatornamegrad f(x) rangle.
$$
It holds that
beginalign*
langle v, operatornamegrad f(x) rangle
&oversetphantomD_vf(x)=D_wf(x)=
sum_i=1^2 v_i cdot fracpartial fpartial x_i
= v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2 \
&oversetD_vf(x)=D_wf(x)=
sum_i=1^2 w_i cdot fracpartial fpartial x_i
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2
= 0
endalign*
Therefore
beginalign*
&,
v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2 \
iff&,
(v_1-w_1) fracpartial fpartial x_1
+ (v_2-w_2) fracpartial fpartial x_2
= 0
endalign*



Since $operatornamegrad f(x) = left(fracpartial fpartial x_1, fracpartial fpartial x_wright) neq 0$ this is a non trivial linear combination of $0$.



Hence $v$ and $w$ are linearly dependent.



Is this proof correct?
I am actually not sure if I used the correct scalar product $langle cdot, cdot rangle$.



Thanks in advance.







share|cite|improve this question






















  • $(v_1-w_1),f_1^(1)+(v_2-w_2),f^(2)$ is not a linear combination of the vectors $v$ and $w$, nor does it imply that they are linearly dependent. E.g., if $nabla f=(1,1)$, then this quantity vanishes for $v=(1,0)$ and $w=(0,1)$.
    – amd
    Aug 25 at 1:27











  • @amd Thanks for pointing that out. Is the approach not suitable in general? How can I proof this instead?
    – Cornman
    Aug 25 at 1:37











  • Both $v$ and $w$ satisfy a particular homogeneous linear equation. What can you say about that equation’s solution space?
    – amd
    Aug 25 at 2:34










  • @amd I looked into my lecture notes, but I did not find anything about a homogeneous linear equation, which $v$ and $w$ satisfy. Do you have a source on this topic? Or do you just mean the quoted theorem, which gives: $beginpmatrix v_1& v_2\ w_1&w_2endpmatrixcdotoperatornamegradf(x)=beginpmatrix0\0endpmatrix$
    – Cornman
    Aug 25 at 3:31














up vote
0
down vote

favorite













Let $f colon mathbbR^2 to mathbbR$ be continuous differentiable and $x inmathbbR^2$ with $operatornamegrad f(x) neq 0$. Let $v,w in mathbbR^2$ be direction vectors.



If $D_v f(x) = D_w f(x)=0$ then are $v$ and $w$ linearly dependent.




For my proof I use the following theorem:




Let $U subset mathbbR^m$ be open and $f colon U to mathbbR$ continuous differentiable. Then it holds for $x in U$ and $v in mathbbR^m$ with $|v| = 1$ that $D_v f(x) = langle v, operatornamegrad f(x) rangle$.




Proof:



With the theorem above and the assumption, we know that
$$
langle v, operatornamegrad f(x) rangle
= langle w, operatornamegrad f(x) rangle.
$$
It holds that
beginalign*
langle v, operatornamegrad f(x) rangle
&oversetphantomD_vf(x)=D_wf(x)=
sum_i=1^2 v_i cdot fracpartial fpartial x_i
= v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2 \
&oversetD_vf(x)=D_wf(x)=
sum_i=1^2 w_i cdot fracpartial fpartial x_i
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2
= 0
endalign*
Therefore
beginalign*
&,
v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2 \
iff&,
(v_1-w_1) fracpartial fpartial x_1
+ (v_2-w_2) fracpartial fpartial x_2
= 0
endalign*



Since $operatornamegrad f(x) = left(fracpartial fpartial x_1, fracpartial fpartial x_wright) neq 0$ this is a non trivial linear combination of $0$.



Hence $v$ and $w$ are linearly dependent.



Is this proof correct?
I am actually not sure if I used the correct scalar product $langle cdot, cdot rangle$.



Thanks in advance.







share|cite|improve this question






















  • $(v_1-w_1),f_1^(1)+(v_2-w_2),f^(2)$ is not a linear combination of the vectors $v$ and $w$, nor does it imply that they are linearly dependent. E.g., if $nabla f=(1,1)$, then this quantity vanishes for $v=(1,0)$ and $w=(0,1)$.
    – amd
    Aug 25 at 1:27











  • @amd Thanks for pointing that out. Is the approach not suitable in general? How can I proof this instead?
    – Cornman
    Aug 25 at 1:37











  • Both $v$ and $w$ satisfy a particular homogeneous linear equation. What can you say about that equation’s solution space?
    – amd
    Aug 25 at 2:34










  • @amd I looked into my lecture notes, but I did not find anything about a homogeneous linear equation, which $v$ and $w$ satisfy. Do you have a source on this topic? Or do you just mean the quoted theorem, which gives: $beginpmatrix v_1& v_2\ w_1&w_2endpmatrixcdotoperatornamegradf(x)=beginpmatrix0\0endpmatrix$
    – Cornman
    Aug 25 at 3:31












up vote
0
down vote

favorite









up vote
0
down vote

favorite












Let $f colon mathbbR^2 to mathbbR$ be continuous differentiable and $x inmathbbR^2$ with $operatornamegrad f(x) neq 0$. Let $v,w in mathbbR^2$ be direction vectors.



If $D_v f(x) = D_w f(x)=0$ then are $v$ and $w$ linearly dependent.




For my proof I use the following theorem:




Let $U subset mathbbR^m$ be open and $f colon U to mathbbR$ continuous differentiable. Then it holds for $x in U$ and $v in mathbbR^m$ with $|v| = 1$ that $D_v f(x) = langle v, operatornamegrad f(x) rangle$.




Proof:



With the theorem above and the assumption, we know that
$$
langle v, operatornamegrad f(x) rangle
= langle w, operatornamegrad f(x) rangle.
$$
It holds that
beginalign*
langle v, operatornamegrad f(x) rangle
&oversetphantomD_vf(x)=D_wf(x)=
sum_i=1^2 v_i cdot fracpartial fpartial x_i
= v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2 \
&oversetD_vf(x)=D_wf(x)=
sum_i=1^2 w_i cdot fracpartial fpartial x_i
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2
= 0
endalign*
Therefore
beginalign*
&,
v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2 \
iff&,
(v_1-w_1) fracpartial fpartial x_1
+ (v_2-w_2) fracpartial fpartial x_2
= 0
endalign*



Since $operatornamegrad f(x) = left(fracpartial fpartial x_1, fracpartial fpartial x_wright) neq 0$ this is a non trivial linear combination of $0$.



Hence $v$ and $w$ are linearly dependent.



Is this proof correct?
I am actually not sure if I used the correct scalar product $langle cdot, cdot rangle$.



Thanks in advance.







share|cite|improve this question















Let $f colon mathbbR^2 to mathbbR$ be continuous differentiable and $x inmathbbR^2$ with $operatornamegrad f(x) neq 0$. Let $v,w in mathbbR^2$ be direction vectors.



If $D_v f(x) = D_w f(x)=0$ then are $v$ and $w$ linearly dependent.




For my proof I use the following theorem:




Let $U subset mathbbR^m$ be open and $f colon U to mathbbR$ continuous differentiable. Then it holds for $x in U$ and $v in mathbbR^m$ with $|v| = 1$ that $D_v f(x) = langle v, operatornamegrad f(x) rangle$.




Proof:



With the theorem above and the assumption, we know that
$$
langle v, operatornamegrad f(x) rangle
= langle w, operatornamegrad f(x) rangle.
$$
It holds that
beginalign*
langle v, operatornamegrad f(x) rangle
&oversetphantomD_vf(x)=D_wf(x)=
sum_i=1^2 v_i cdot fracpartial fpartial x_i
= v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2 \
&oversetD_vf(x)=D_wf(x)=
sum_i=1^2 w_i cdot fracpartial fpartial x_i
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2
= 0
endalign*
Therefore
beginalign*
&,
v_1 cdot fracpartial fpartial x_1
+ v_2 cdot fracpartial fpartial x_2
= w_1 cdot fracpartial fpartial x_1
+ w_2 cdot fracpartial fpartial x_2 \
iff&,
(v_1-w_1) fracpartial fpartial x_1
+ (v_2-w_2) fracpartial fpartial x_2
= 0
endalign*



Since $operatornamegrad f(x) = left(fracpartial fpartial x_1, fracpartial fpartial x_wright) neq 0$ this is a non trivial linear combination of $0$.



Hence $v$ and $w$ are linearly dependent.



Is this proof correct?
I am actually not sure if I used the correct scalar product $langle cdot, cdot rangle$.



Thanks in advance.









share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Aug 25 at 12:46









Jendrik Stelzner

7,57221037




7,57221037










asked Aug 25 at 1:03









Cornman

2,70421228




2,70421228











  • $(v_1-w_1),f_1^(1)+(v_2-w_2),f^(2)$ is not a linear combination of the vectors $v$ and $w$, nor does it imply that they are linearly dependent. E.g., if $nabla f=(1,1)$, then this quantity vanishes for $v=(1,0)$ and $w=(0,1)$.
    – amd
    Aug 25 at 1:27











  • @amd Thanks for pointing that out. Is the approach not suitable in general? How can I proof this instead?
    – Cornman
    Aug 25 at 1:37











  • Both $v$ and $w$ satisfy a particular homogeneous linear equation. What can you say about that equation’s solution space?
    – amd
    Aug 25 at 2:34










  • @amd I looked into my lecture notes, but I did not find anything about a homogeneous linear equation, which $v$ and $w$ satisfy. Do you have a source on this topic? Or do you just mean the quoted theorem, which gives: $beginpmatrix v_1& v_2\ w_1&w_2endpmatrixcdotoperatornamegradf(x)=beginpmatrix0\0endpmatrix$
    – Cornman
    Aug 25 at 3:31
















  • $(v_1-w_1),f_1^(1)+(v_2-w_2),f^(2)$ is not a linear combination of the vectors $v$ and $w$, nor does it imply that they are linearly dependent. E.g., if $nabla f=(1,1)$, then this quantity vanishes for $v=(1,0)$ and $w=(0,1)$.
    – amd
    Aug 25 at 1:27











  • @amd Thanks for pointing that out. Is the approach not suitable in general? How can I proof this instead?
    – Cornman
    Aug 25 at 1:37











  • Both $v$ and $w$ satisfy a particular homogeneous linear equation. What can you say about that equation’s solution space?
    – amd
    Aug 25 at 2:34










  • @amd I looked into my lecture notes, but I did not find anything about a homogeneous linear equation, which $v$ and $w$ satisfy. Do you have a source on this topic? Or do you just mean the quoted theorem, which gives: $beginpmatrix v_1& v_2\ w_1&w_2endpmatrixcdotoperatornamegradf(x)=beginpmatrix0\0endpmatrix$
    – Cornman
    Aug 25 at 3:31















$(v_1-w_1),f_1^(1)+(v_2-w_2),f^(2)$ is not a linear combination of the vectors $v$ and $w$, nor does it imply that they are linearly dependent. E.g., if $nabla f=(1,1)$, then this quantity vanishes for $v=(1,0)$ and $w=(0,1)$.
– amd
Aug 25 at 1:27





$(v_1-w_1),f_1^(1)+(v_2-w_2),f^(2)$ is not a linear combination of the vectors $v$ and $w$, nor does it imply that they are linearly dependent. E.g., if $nabla f=(1,1)$, then this quantity vanishes for $v=(1,0)$ and $w=(0,1)$.
– amd
Aug 25 at 1:27













@amd Thanks for pointing that out. Is the approach not suitable in general? How can I proof this instead?
– Cornman
Aug 25 at 1:37





@amd Thanks for pointing that out. Is the approach not suitable in general? How can I proof this instead?
– Cornman
Aug 25 at 1:37













Both $v$ and $w$ satisfy a particular homogeneous linear equation. What can you say about that equation’s solution space?
– amd
Aug 25 at 2:34




Both $v$ and $w$ satisfy a particular homogeneous linear equation. What can you say about that equation’s solution space?
– amd
Aug 25 at 2:34












@amd I looked into my lecture notes, but I did not find anything about a homogeneous linear equation, which $v$ and $w$ satisfy. Do you have a source on this topic? Or do you just mean the quoted theorem, which gives: $beginpmatrix v_1& v_2\ w_1&w_2endpmatrixcdotoperatornamegradf(x)=beginpmatrix0\0endpmatrix$
– Cornman
Aug 25 at 3:31




@amd I looked into my lecture notes, but I did not find anything about a homogeneous linear equation, which $v$ and $w$ satisfy. Do you have a source on this topic? Or do you just mean the quoted theorem, which gives: $beginpmatrix v_1& v_2\ w_1&w_2endpmatrixcdotoperatornamegradf(x)=beginpmatrix0\0endpmatrix$
– Cornman
Aug 25 at 3:31










1 Answer
1






active

oldest

votes

















up vote
1
down vote



accepted










For given $x$ in the domain of $f$ the derivative $df(x)$ (also denoted by $f'(x)$, or similar) is a linear map acting as
$$df(x):quad mathbb R^2tomathbb R,qquad Xmapstolanglenabla f(x),Xrangle ,$$
whereby $langlecdot,cdotrangle$ denotes the standard scalar product in $mathbb R^2$, as in the question.
Since $nabla f(x)ne0$ by assumption this map has rank $1$; hence its kernel $K$ has dimension $2-1=1$. We are told that both $v$ and $w$ are in $K$; therefore $v$ and $w$ have to be linearly dependent.



Note that this argument does not work for an $f:>mathbb R^ntomathbb R$ with $n>2$.






share|cite|improve this answer






















  • How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
    – Cornman
    Aug 25 at 15:06










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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes








up vote
1
down vote



accepted










For given $x$ in the domain of $f$ the derivative $df(x)$ (also denoted by $f'(x)$, or similar) is a linear map acting as
$$df(x):quad mathbb R^2tomathbb R,qquad Xmapstolanglenabla f(x),Xrangle ,$$
whereby $langlecdot,cdotrangle$ denotes the standard scalar product in $mathbb R^2$, as in the question.
Since $nabla f(x)ne0$ by assumption this map has rank $1$; hence its kernel $K$ has dimension $2-1=1$. We are told that both $v$ and $w$ are in $K$; therefore $v$ and $w$ have to be linearly dependent.



Note that this argument does not work for an $f:>mathbb R^ntomathbb R$ with $n>2$.






share|cite|improve this answer






















  • How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
    – Cornman
    Aug 25 at 15:06














up vote
1
down vote



accepted










For given $x$ in the domain of $f$ the derivative $df(x)$ (also denoted by $f'(x)$, or similar) is a linear map acting as
$$df(x):quad mathbb R^2tomathbb R,qquad Xmapstolanglenabla f(x),Xrangle ,$$
whereby $langlecdot,cdotrangle$ denotes the standard scalar product in $mathbb R^2$, as in the question.
Since $nabla f(x)ne0$ by assumption this map has rank $1$; hence its kernel $K$ has dimension $2-1=1$. We are told that both $v$ and $w$ are in $K$; therefore $v$ and $w$ have to be linearly dependent.



Note that this argument does not work for an $f:>mathbb R^ntomathbb R$ with $n>2$.






share|cite|improve this answer






















  • How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
    – Cornman
    Aug 25 at 15:06












up vote
1
down vote



accepted







up vote
1
down vote



accepted






For given $x$ in the domain of $f$ the derivative $df(x)$ (also denoted by $f'(x)$, or similar) is a linear map acting as
$$df(x):quad mathbb R^2tomathbb R,qquad Xmapstolanglenabla f(x),Xrangle ,$$
whereby $langlecdot,cdotrangle$ denotes the standard scalar product in $mathbb R^2$, as in the question.
Since $nabla f(x)ne0$ by assumption this map has rank $1$; hence its kernel $K$ has dimension $2-1=1$. We are told that both $v$ and $w$ are in $K$; therefore $v$ and $w$ have to be linearly dependent.



Note that this argument does not work for an $f:>mathbb R^ntomathbb R$ with $n>2$.






share|cite|improve this answer














For given $x$ in the domain of $f$ the derivative $df(x)$ (also denoted by $f'(x)$, or similar) is a linear map acting as
$$df(x):quad mathbb R^2tomathbb R,qquad Xmapstolanglenabla f(x),Xrangle ,$$
whereby $langlecdot,cdotrangle$ denotes the standard scalar product in $mathbb R^2$, as in the question.
Since $nabla f(x)ne0$ by assumption this map has rank $1$; hence its kernel $K$ has dimension $2-1=1$. We are told that both $v$ and $w$ are in $K$; therefore $v$ and $w$ have to be linearly dependent.



Note that this argument does not work for an $f:>mathbb R^ntomathbb R$ with $n>2$.







share|cite|improve this answer














share|cite|improve this answer



share|cite|improve this answer








edited Aug 25 at 15:13

























answered Aug 25 at 12:28









Christian Blatter

165k7109310




165k7109310











  • How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
    – Cornman
    Aug 25 at 15:06
















  • How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
    – Cornman
    Aug 25 at 15:06















How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
– Cornman
Aug 25 at 15:06




How do we get $df(x).X=langlenabla f(x), Xrangle$ and how is $langlecdot, cdotrangle$ defined here? Standard scalar product on $mathbbR^2$?
– Cornman
Aug 25 at 15:06

















 

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