Solving the Hamilton-Jacobi equation (Evans)












4












$begingroup$


I can comprehend some but not all of the proofs. I do not understand how the limit definition of a derivative is derived in this context, and those are highlighted in $color{#009900}{text{green}}$.



This is from PDE Evans, 2nd edition, pages 127-128.




Theorem 5 (Solving the Hamilton-Jacobi equation). Suppose $x in mathbb{R}^n$, $t > 0$, and $u$ defined by the Hopf-Lax formula $$u(x,t)=min_{yinmathbb{R}^n} left{tLleft(frac{x-y}{t} right) + g(y) right}$$ is differentiable at a point $(x,t) in mathbb{R}^n times (0,infty)$. Then $$u_t(x,t)+H(Du(x,t))=0.$$



Proof. 1.) Fix $v in mathbb{R}^n, h > 0$. Owing to Lemma 1,
begin{align}
u(x+hv,t+h)&=min_{y in mathbb{R}^n} left{ hLleft(frac{x+hv-y}
{h}right)+u(y,t)right} \
&le hL(v)+u(x,t).
end{align}
Hence, $$frac{u(x+hv,t+h)-u(x,t)}{h} le L(v).$$
Let $h rightarrow 0^+$, to compute $$underbrace{v cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is this expression obtained?}}} le L(v).$$
This inequality is valid for all $v in mathbb{R}^n$, and so $$u_t(x,t)+H(Du(x,t))=u_t(x,t)+max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) } le 0. tag{31}$$
The first equality holds since $H = L^*:=max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) }$, by convex duality of Hamiltonian and Lagrangian (page 121 of the book).



2.) Now chose $z$ such that $u(x,t)=tL(frac{x-z}{t})+g(z)$. Fix $h > 0$ and set $s=t-h,y=frac st x+(1- frac st)z$. Then $frac{x-z}{t}=frac{y-z}{s}$, and thus
begin{align}
u(x,t)-u(y,s) &ge tLleft(frac{y-z}{s} right) + g(z) - left[sLleft(frac{y-z}{s} right)+g(z) right] \ &= (t-s)Lleft(frac{y-z}{s} right) \ &=hLleft(frac{y-z}{s} right).
end{align}
That is, $$frac{u(x,t)-u((1-frac ht)x+frac htz,t-h)}{h} ge Lleft(frac{x-z}{t} right).$$
Let $h rightarrow 0^+$, to see that $$underbrace{frac{x-z}{t} cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is the limit definition of derivative applied here exactly?}}} ge Lleft(frac{x-z}{t} right).$$
Consequently,

begin{align}
u_t(x,t)+H(Du(x,t))&=u_t(x,t)+max_{vinmathbb{R}^n} {v cdot Du(x,t)-L(v) } \
&ge u_t(x,t)+frac{x-z}{t} cdot Du(x,t)-Lleft(frac{x-z}{t} right) \ &ge 0
end{align}
This inequality and $text{(31)}$ complete the proof.











share|cite|improve this question











$endgroup$

















    4












    $begingroup$


    I can comprehend some but not all of the proofs. I do not understand how the limit definition of a derivative is derived in this context, and those are highlighted in $color{#009900}{text{green}}$.



    This is from PDE Evans, 2nd edition, pages 127-128.




    Theorem 5 (Solving the Hamilton-Jacobi equation). Suppose $x in mathbb{R}^n$, $t > 0$, and $u$ defined by the Hopf-Lax formula $$u(x,t)=min_{yinmathbb{R}^n} left{tLleft(frac{x-y}{t} right) + g(y) right}$$ is differentiable at a point $(x,t) in mathbb{R}^n times (0,infty)$. Then $$u_t(x,t)+H(Du(x,t))=0.$$



    Proof. 1.) Fix $v in mathbb{R}^n, h > 0$. Owing to Lemma 1,
    begin{align}
    u(x+hv,t+h)&=min_{y in mathbb{R}^n} left{ hLleft(frac{x+hv-y}
    {h}right)+u(y,t)right} \
    &le hL(v)+u(x,t).
    end{align}
    Hence, $$frac{u(x+hv,t+h)-u(x,t)}{h} le L(v).$$
    Let $h rightarrow 0^+$, to compute $$underbrace{v cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is this expression obtained?}}} le L(v).$$
    This inequality is valid for all $v in mathbb{R}^n$, and so $$u_t(x,t)+H(Du(x,t))=u_t(x,t)+max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) } le 0. tag{31}$$
    The first equality holds since $H = L^*:=max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) }$, by convex duality of Hamiltonian and Lagrangian (page 121 of the book).



    2.) Now chose $z$ such that $u(x,t)=tL(frac{x-z}{t})+g(z)$. Fix $h > 0$ and set $s=t-h,y=frac st x+(1- frac st)z$. Then $frac{x-z}{t}=frac{y-z}{s}$, and thus
    begin{align}
    u(x,t)-u(y,s) &ge tLleft(frac{y-z}{s} right) + g(z) - left[sLleft(frac{y-z}{s} right)+g(z) right] \ &= (t-s)Lleft(frac{y-z}{s} right) \ &=hLleft(frac{y-z}{s} right).
    end{align}
    That is, $$frac{u(x,t)-u((1-frac ht)x+frac htz,t-h)}{h} ge Lleft(frac{x-z}{t} right).$$
    Let $h rightarrow 0^+$, to see that $$underbrace{frac{x-z}{t} cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is the limit definition of derivative applied here exactly?}}} ge Lleft(frac{x-z}{t} right).$$
    Consequently,

    begin{align}
    u_t(x,t)+H(Du(x,t))&=u_t(x,t)+max_{vinmathbb{R}^n} {v cdot Du(x,t)-L(v) } \
    &ge u_t(x,t)+frac{x-z}{t} cdot Du(x,t)-Lleft(frac{x-z}{t} right) \ &ge 0
    end{align}
    This inequality and $text{(31)}$ complete the proof.











    share|cite|improve this question











    $endgroup$















      4












      4








      4


      2



      $begingroup$


      I can comprehend some but not all of the proofs. I do not understand how the limit definition of a derivative is derived in this context, and those are highlighted in $color{#009900}{text{green}}$.



      This is from PDE Evans, 2nd edition, pages 127-128.




      Theorem 5 (Solving the Hamilton-Jacobi equation). Suppose $x in mathbb{R}^n$, $t > 0$, and $u$ defined by the Hopf-Lax formula $$u(x,t)=min_{yinmathbb{R}^n} left{tLleft(frac{x-y}{t} right) + g(y) right}$$ is differentiable at a point $(x,t) in mathbb{R}^n times (0,infty)$. Then $$u_t(x,t)+H(Du(x,t))=0.$$



      Proof. 1.) Fix $v in mathbb{R}^n, h > 0$. Owing to Lemma 1,
      begin{align}
      u(x+hv,t+h)&=min_{y in mathbb{R}^n} left{ hLleft(frac{x+hv-y}
      {h}right)+u(y,t)right} \
      &le hL(v)+u(x,t).
      end{align}
      Hence, $$frac{u(x+hv,t+h)-u(x,t)}{h} le L(v).$$
      Let $h rightarrow 0^+$, to compute $$underbrace{v cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is this expression obtained?}}} le L(v).$$
      This inequality is valid for all $v in mathbb{R}^n$, and so $$u_t(x,t)+H(Du(x,t))=u_t(x,t)+max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) } le 0. tag{31}$$
      The first equality holds since $H = L^*:=max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) }$, by convex duality of Hamiltonian and Lagrangian (page 121 of the book).



      2.) Now chose $z$ such that $u(x,t)=tL(frac{x-z}{t})+g(z)$. Fix $h > 0$ and set $s=t-h,y=frac st x+(1- frac st)z$. Then $frac{x-z}{t}=frac{y-z}{s}$, and thus
      begin{align}
      u(x,t)-u(y,s) &ge tLleft(frac{y-z}{s} right) + g(z) - left[sLleft(frac{y-z}{s} right)+g(z) right] \ &= (t-s)Lleft(frac{y-z}{s} right) \ &=hLleft(frac{y-z}{s} right).
      end{align}
      That is, $$frac{u(x,t)-u((1-frac ht)x+frac htz,t-h)}{h} ge Lleft(frac{x-z}{t} right).$$
      Let $h rightarrow 0^+$, to see that $$underbrace{frac{x-z}{t} cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is the limit definition of derivative applied here exactly?}}} ge Lleft(frac{x-z}{t} right).$$
      Consequently,

      begin{align}
      u_t(x,t)+H(Du(x,t))&=u_t(x,t)+max_{vinmathbb{R}^n} {v cdot Du(x,t)-L(v) } \
      &ge u_t(x,t)+frac{x-z}{t} cdot Du(x,t)-Lleft(frac{x-z}{t} right) \ &ge 0
      end{align}
      This inequality and $text{(31)}$ complete the proof.











      share|cite|improve this question











      $endgroup$




      I can comprehend some but not all of the proofs. I do not understand how the limit definition of a derivative is derived in this context, and those are highlighted in $color{#009900}{text{green}}$.



      This is from PDE Evans, 2nd edition, pages 127-128.




      Theorem 5 (Solving the Hamilton-Jacobi equation). Suppose $x in mathbb{R}^n$, $t > 0$, and $u$ defined by the Hopf-Lax formula $$u(x,t)=min_{yinmathbb{R}^n} left{tLleft(frac{x-y}{t} right) + g(y) right}$$ is differentiable at a point $(x,t) in mathbb{R}^n times (0,infty)$. Then $$u_t(x,t)+H(Du(x,t))=0.$$



      Proof. 1.) Fix $v in mathbb{R}^n, h > 0$. Owing to Lemma 1,
      begin{align}
      u(x+hv,t+h)&=min_{y in mathbb{R}^n} left{ hLleft(frac{x+hv-y}
      {h}right)+u(y,t)right} \
      &le hL(v)+u(x,t).
      end{align}
      Hence, $$frac{u(x+hv,t+h)-u(x,t)}{h} le L(v).$$
      Let $h rightarrow 0^+$, to compute $$underbrace{v cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is this expression obtained?}}} le L(v).$$
      This inequality is valid for all $v in mathbb{R}^n$, and so $$u_t(x,t)+H(Du(x,t))=u_t(x,t)+max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) } le 0. tag{31}$$
      The first equality holds since $H = L^*:=max_{v in mathbb{R}^n} {v cdot Du(x,t)-L(v) }$, by convex duality of Hamiltonian and Lagrangian (page 121 of the book).



      2.) Now chose $z$ such that $u(x,t)=tL(frac{x-z}{t})+g(z)$. Fix $h > 0$ and set $s=t-h,y=frac st x+(1- frac st)z$. Then $frac{x-z}{t}=frac{y-z}{s}$, and thus
      begin{align}
      u(x,t)-u(y,s) &ge tLleft(frac{y-z}{s} right) + g(z) - left[sLleft(frac{y-z}{s} right)+g(z) right] \ &= (t-s)Lleft(frac{y-z}{s} right) \ &=hLleft(frac{y-z}{s} right).
      end{align}
      That is, $$frac{u(x,t)-u((1-frac ht)x+frac htz,t-h)}{h} ge Lleft(frac{x-z}{t} right).$$
      Let $h rightarrow 0^+$, to see that $$underbrace{frac{x-z}{t} cdot Du(x,t)+u_t(x,t)}_{color{#009900}{text{How is the limit definition of derivative applied here exactly?}}} ge Lleft(frac{x-z}{t} right).$$
      Consequently,

      begin{align}
      u_t(x,t)+H(Du(x,t))&=u_t(x,t)+max_{vinmathbb{R}^n} {v cdot Du(x,t)-L(v) } \
      &ge u_t(x,t)+frac{x-z}{t} cdot Du(x,t)-Lleft(frac{x-z}{t} right) \ &ge 0
      end{align}
      This inequality and $text{(31)}$ complete the proof.








      limits pde hamilton-jacobi-equation






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      edited Dec 11 '18 at 11:29









      Harry49

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      asked Jun 18 '14 at 2:19









      CookieCookie

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          1 Answer
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          active

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          1












          $begingroup$

          For $f:mathbb{R}^n times mathbb{R}^+ rightarrow mathbb{R}$, the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$ such that



          $$|f(x+v,t +s)-f(x,t) -hat{D}f(x,t)cdot (v,s)| = o(|(v,s)|),$$



          as $|(v,s)| rightarrow 0.$



          The $1 times (n+1)$ matrix of this operator with respect to the canonical basis has the partial derivatives as components:



          $$[Df(x,t)]= (f_{x_1},f_{x_2},ldots,f_{x_n},f_{t}).$$



          This implies for $s=h > 0$



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} =hat{D}f(x,t)cdot (v,1),$$



          or



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} ={D}f(x,t)cdot v + f_{t}(x,t),$$



          where the $1 times n$ matrix of $D$ has the partial derivatives with respect to $x_1,ldots,x_n$ as components.






          share|cite|improve this answer











          $endgroup$













          • $begingroup$
            Basically in this case $D$ is the gradient operator.
            $endgroup$
            – RRL
            Jun 18 '14 at 2:58










          • $begingroup$
            "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
            $endgroup$
            – Cookie
            Jun 18 '14 at 6:33






          • 1




            $begingroup$
            I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
            $endgroup$
            – RRL
            Jun 18 '14 at 6:57










          • $begingroup$
            It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:04






          • 1




            $begingroup$
            Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:30













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          1 Answer
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          active

          oldest

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          active

          oldest

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          1












          $begingroup$

          For $f:mathbb{R}^n times mathbb{R}^+ rightarrow mathbb{R}$, the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$ such that



          $$|f(x+v,t +s)-f(x,t) -hat{D}f(x,t)cdot (v,s)| = o(|(v,s)|),$$



          as $|(v,s)| rightarrow 0.$



          The $1 times (n+1)$ matrix of this operator with respect to the canonical basis has the partial derivatives as components:



          $$[Df(x,t)]= (f_{x_1},f_{x_2},ldots,f_{x_n},f_{t}).$$



          This implies for $s=h > 0$



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} =hat{D}f(x,t)cdot (v,1),$$



          or



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} ={D}f(x,t)cdot v + f_{t}(x,t),$$



          where the $1 times n$ matrix of $D$ has the partial derivatives with respect to $x_1,ldots,x_n$ as components.






          share|cite|improve this answer











          $endgroup$













          • $begingroup$
            Basically in this case $D$ is the gradient operator.
            $endgroup$
            – RRL
            Jun 18 '14 at 2:58










          • $begingroup$
            "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
            $endgroup$
            – Cookie
            Jun 18 '14 at 6:33






          • 1




            $begingroup$
            I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
            $endgroup$
            – RRL
            Jun 18 '14 at 6:57










          • $begingroup$
            It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:04






          • 1




            $begingroup$
            Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:30


















          1












          $begingroup$

          For $f:mathbb{R}^n times mathbb{R}^+ rightarrow mathbb{R}$, the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$ such that



          $$|f(x+v,t +s)-f(x,t) -hat{D}f(x,t)cdot (v,s)| = o(|(v,s)|),$$



          as $|(v,s)| rightarrow 0.$



          The $1 times (n+1)$ matrix of this operator with respect to the canonical basis has the partial derivatives as components:



          $$[Df(x,t)]= (f_{x_1},f_{x_2},ldots,f_{x_n},f_{t}).$$



          This implies for $s=h > 0$



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} =hat{D}f(x,t)cdot (v,1),$$



          or



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} ={D}f(x,t)cdot v + f_{t}(x,t),$$



          where the $1 times n$ matrix of $D$ has the partial derivatives with respect to $x_1,ldots,x_n$ as components.






          share|cite|improve this answer











          $endgroup$













          • $begingroup$
            Basically in this case $D$ is the gradient operator.
            $endgroup$
            – RRL
            Jun 18 '14 at 2:58










          • $begingroup$
            "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
            $endgroup$
            – Cookie
            Jun 18 '14 at 6:33






          • 1




            $begingroup$
            I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
            $endgroup$
            – RRL
            Jun 18 '14 at 6:57










          • $begingroup$
            It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:04






          • 1




            $begingroup$
            Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:30
















          1












          1








          1





          $begingroup$

          For $f:mathbb{R}^n times mathbb{R}^+ rightarrow mathbb{R}$, the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$ such that



          $$|f(x+v,t +s)-f(x,t) -hat{D}f(x,t)cdot (v,s)| = o(|(v,s)|),$$



          as $|(v,s)| rightarrow 0.$



          The $1 times (n+1)$ matrix of this operator with respect to the canonical basis has the partial derivatives as components:



          $$[Df(x,t)]= (f_{x_1},f_{x_2},ldots,f_{x_n},f_{t}).$$



          This implies for $s=h > 0$



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} =hat{D}f(x,t)cdot (v,1),$$



          or



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} ={D}f(x,t)cdot v + f_{t}(x,t),$$



          where the $1 times n$ matrix of $D$ has the partial derivatives with respect to $x_1,ldots,x_n$ as components.






          share|cite|improve this answer











          $endgroup$



          For $f:mathbb{R}^n times mathbb{R}^+ rightarrow mathbb{R}$, the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$ such that



          $$|f(x+v,t +s)-f(x,t) -hat{D}f(x,t)cdot (v,s)| = o(|(v,s)|),$$



          as $|(v,s)| rightarrow 0.$



          The $1 times (n+1)$ matrix of this operator with respect to the canonical basis has the partial derivatives as components:



          $$[Df(x,t)]= (f_{x_1},f_{x_2},ldots,f_{x_n},f_{t}).$$



          This implies for $s=h > 0$



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} =hat{D}f(x,t)cdot (v,1),$$



          or



          $$lim_{h rightarrow 0} frac{f(x+hv,t +h)-f(x,t)}{h} ={D}f(x,t)cdot v + f_{t}(x,t),$$



          where the $1 times n$ matrix of $D$ has the partial derivatives with respect to $x_1,ldots,x_n$ as components.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Jun 18 '14 at 6:45

























          answered Jun 18 '14 at 2:55









          RRLRRL

          51.6k42573




          51.6k42573












          • $begingroup$
            Basically in this case $D$ is the gradient operator.
            $endgroup$
            – RRL
            Jun 18 '14 at 2:58










          • $begingroup$
            "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
            $endgroup$
            – Cookie
            Jun 18 '14 at 6:33






          • 1




            $begingroup$
            I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
            $endgroup$
            – RRL
            Jun 18 '14 at 6:57










          • $begingroup$
            It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:04






          • 1




            $begingroup$
            Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:30




















          • $begingroup$
            Basically in this case $D$ is the gradient operator.
            $endgroup$
            – RRL
            Jun 18 '14 at 2:58










          • $begingroup$
            "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
            $endgroup$
            – Cookie
            Jun 18 '14 at 6:33






          • 1




            $begingroup$
            I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
            $endgroup$
            – RRL
            Jun 18 '14 at 6:57










          • $begingroup$
            It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:04






          • 1




            $begingroup$
            Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
            $endgroup$
            – RRL
            Jun 18 '14 at 7:30


















          $begingroup$
          Basically in this case $D$ is the gradient operator.
          $endgroup$
          – RRL
          Jun 18 '14 at 2:58




          $begingroup$
          Basically in this case $D$ is the gradient operator.
          $endgroup$
          – RRL
          Jun 18 '14 at 2:58












          $begingroup$
          "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
          $endgroup$
          – Cookie
          Jun 18 '14 at 6:33




          $begingroup$
          "...the derivative at a point $(x,t)$ is a linear operator mapping $(v,s) in mathbb{R}^n times mathbb{R}^+$ into $hat{D}f(x,t)cdot (v,s) in mathbb{R}$..." I understand most of your answer, but I have one small question: I am not too familiar with functional analysis yet; why do we get the byproduct of $(v,s)$? Is this simply the result from the definition of taking a derivative of a function $f(x,t)$ in $mathbb{R}^n timesmathbb{R}^+$?
          $endgroup$
          – Cookie
          Jun 18 '14 at 6:33




          1




          1




          $begingroup$
          I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
          $endgroup$
          – RRL
          Jun 18 '14 at 6:57




          $begingroup$
          I made some changes to make it clearer. The definition of a multivariate derivative means if you add an incremental vector $(v,s)$ to $(x,t)$ then $f(x+v,t+s)$ has a linear(affine) approximation with an error that goes to zero as the norm of $(v,s)$ goes to 0. In your problem you have $(hv,h)$ as the increment which goes to zero in norm iff $h rightarrow 0.$ Dividing the linear approximation by $h$, using linearity $hat{D}(x,t)(hv,h) = hhat{D}(x,t)(v,1)$, and taking the limit as $h rightarrow 0$ you get the result you want -- since the error term goes to 0.
          $endgroup$
          – RRL
          Jun 18 '14 at 6:57












          $begingroup$
          It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
          $endgroup$
          – RRL
          Jun 18 '14 at 7:04




          $begingroup$
          It's analagous to $f(x+h) - f(x) - f'(x)h = e(h)$ where $e(h) rightarrow 0$ as $h rightarrow 0$ in the single variable case. This is equivalent to $lim [f(x+h)-f(x)]/h = f'(x).$
          $endgroup$
          – RRL
          Jun 18 '14 at 7:04




          1




          1




          $begingroup$
          Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
          $endgroup$
          – RRL
          Jun 18 '14 at 7:30






          $begingroup$
          Not quite. $u(x+hv,t+h) = u(x,t) + Lcdot(hv,h) + ho(h) = u(x,t) +hLcdot(v,1) + ho(h)$ where $L = hat{D}(x,t)$ is the linear operator. In matrix terms $Lcdot(v,1) = (u_{x_1},ldots,u_{x_n},u_t)cdot(v_1,dots,v_n,1)$. Then $lim[u(x+hv,t+h)-u(x,t)]/h = Lcdot(v,1)$
          $endgroup$
          – RRL
          Jun 18 '14 at 7:30




















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