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\noindent {\bf Math 273}: {\bf Homework \#3, due on Monday, November 8} 

\bigbreak 

\noindent{\bf [1]} Let $\Omega$ be an open and bounded domain in $\R^2$, with sufficiently smooth boundary $\partial\Omega$. Consider the minimization problem in two dimensions 
$$\inf_{u}F(u)=\int_{\Omega} (Ku-u_0)^2dxdy+\alpha\int_{\Omega}f(\nabla u)dxdy,$$
with $u_0\in L^2(\Omega)$ (square integrable function) a given function, and $f$ is a smooth function on $\R^2$ with real values. Here $K:L^2(\Omega)\rightarrow L^2(\Omega)$ is a linear and continuous operator, and its adjoint is $K^*$ (thus $K^*$ has the property $\int_{\Omega}(Ku)v dxdy =\int_{\Omega}u (K^*v)dxdy$). 
Obtain, as in Hw\#1[5] and Hw\#2[1], the Euler-Lagrange equation associated with the minimization problem that is formally satisfied by a sufficiently smooth optimal $u$. No explicit boundary conditions are imposed, thus you have to deduce implicit (or natural) boundary conditions on $\partial\Omega$. 

\bigbreak 

\noindent{\bf [2]} (This problem is related with [5] from Hw \#1) 

Let $u(x,y,t)$ be a smooth solution of the time-dependent PDE 
$$\frac{\partial u}{\partial t}=\frac{\partial}{\partial x} L_{u_x}(P)+\frac{\partial}{\partial y}L_{u_y}(P)-L_u(P),$$
with $u(x,y,0)=u_0(x,y)$ in $\Omega$ and $u(x,y,t)=g(x,y)$ for $(x,y)\in \partial\Omega$ and $t\geq 0$ (recall that $P$ is a notation for $(x,y,u(x,y),u_x(x,y),u_y(x,y))$). 

Show that the function $E(t)=F(u(\cdot,\cdot,t))$ is decreasing in time, where 
$F(u)=\int_{\Omega}L(x,y,u,u_x,u_y)dxdy.$

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\noindent{\bf [3]} Apply the gradient descent method described in class to the 
two-dimensional diffusion problem
$$F(u)=\sum_{1\leq i,j\leq n}\Big[(u_{i+1,j}-u_{i,j})^2+(u_{i,j+1}-u_{i,j})^2
+\lambda(u_{i,j}-f_{i,j})^2\Big],$$
where $f_{i,j}$ is given for $0\leq i,j\leq n+1$, and with boundary 
conditions $u_{i,j}=f_{i,j}$ if $i=0$ or $i=n+1$ or $j=0$ or $j=n+1$ (chosen for simplicity). Here $\lambda>0$ is a tunning parameter.
Choose a function $f$ of your choice (for example an image). If you do not have
 one, you can create a synthetic image. Test various values of the parameter 
$\lambda$ and observe the properties of your implementation. Give your choice of the stopping criterion and also plot the value of the objective function versus steps. Plot the data $f$, your starting point and your final result, as 2D 
images.

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\noindent{\bf [4]} Consider the constrained optimization problem 
$$\min_{x\in \R^n} f(x),\ \ \mbox{ subject to }Ax=b,$$
where $A\in \R^{m\times n}$ has full row rank, $m\leq n$, $b\in \R^m$. Tranform the problem into an unconstrained minimization problem by one of the methods discussed in class. 

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\noindent{\bf [5]} Verify that the KKT conditions (1st order optimality conditions) for the bound-constrained problem
$$\min_{x\in \R^n} \phi(x),\mbox{ subject to } l\leq x\leq u,$$
are equivalent to the compactly stated condition
$P_{[l,u]}\nabla \phi(x)=0,$
where the projection operator $P_{[l,u]}$ of a vector $g\in \R^n$ onto the 
rectangular box $[l,u]$ is  defined by
$$
(P_{[l,u]}g)_i=\left\{
\begin{array}{ll}
min(0,g_i), & \mbox{ if } x_i=l_i,\\
g_i, & \mbox{ if } x_i\in (l_i,u_i), \mbox{ for all } i=1,2,...,n\\ 
max(0,g_i), & \mbox{ if } x_i=u_i.
\end{array}
\right.
$$


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