Euler-Lagrange, Gradient Descent, Heat Equation and Image Denoising For a image denoising problem (below):
http://www.bekirdizdaroglu.com/ceng/Downloads/ISCE10.pdf
the author has a functional E defined 
$E(u) = \int\int_\Omega F \\ d\Omega$
which he wants to minimize. F is defined as 
$F = ||\nabla u ||^2 = u_x^2 + u_y^2$
Then, the E-L equations are derived:
$\frac{\partial E}{\partial u} = \frac{\partial F}{\partial u} - 
\frac{d}{dx} \frac{\partial F}{\partial u_x} -
\frac{d}{dy} \frac{\partial F}{\partial u_y} = 0$
Then it is mentioned that gradient descent method is used to minimize the functional E by using 
$\frac{\partial u}{\partial t} = u_{xx} + u_{yy}$ 
which is the heat equation. I understand both equations, and have solved the heat equation numerically before. I also worked with functionals. I do not understand however how the author jumps from the E-L equations to the gradient descent method. How is the time variable t included? Any detailed derivation, proof on this relation would be welcome. I found some papers on the Net, the one by Colding et al looked promising. 
References:
http://arxiv.org/pdf/1102.1411 (Colding et al)
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.1675&rep=rep1&type=pdf (ROF)
http://dl.dropbox.com/u/1570604/tmp/functional-grad-descent.pdf
http://dl.dropbox.com/u/1570604/tmp/gelfand_var_time.ps (Gelfand and Romin)
 A: The equation
$
u_t = u_{xx} + u_{yy} 
$
is a gradient flow, or gradient descent, in the following sense. You should think of the equation as being placed in the space $L^2$. The Fréchèt derivative of the functional $E$ is the linear mapping
$ 
\displaystyle E'(u):  v \mapsto -2\iint \nabla u\cdot \nabla v = -2\iint v(u_{xx}+u_{yy}),
$
where for simplicity I'm assuming that the boundary conditions don't give rise to boundary terms in the partial integration. The second version, after partial integration, is relevant because it's written in the form of  an $L^2$ inner product, allowing us to write the Fréchèt derivative as
$
E'(u)\cdot v = (-2(u_{xx}+u_{yy}),v)_{L^2} =: (\mathrm{grad}\ E(u),v)_{L^2}
$
The $L^2$-gradient flow of $E$ is then the equation
$
u_t = -\mathrm{grad}\  E(u) = 2(u_{xx}+u_{yy}).
$
A: If the solution to 
$
u_t=u_{xx}+u_{yy}
$
reaches an equilibrium solution, then $u_{t}=0$ at that equilibrium, so $u_{xx}+u_{yy}=0$.  The author has shown that a necessary condition for $u$ to be minimizer of $E(u)$ is $u_{xx}+u_{yy}=0$.   
This isn't steepest descent in the way that it is normally presented as an optimization algorithm for minimizing a function $f(x)$, but it is conceptually the same.
Of course you don't want to simply minimize $E(u)$ without respecting the original image- you want to somehow balance the minimization of $E(u)$ with keeping the original image.  By starting the time dependent PDE with the original image and then stopping after a finite time, you can achieve this balance.  
