## Tuesday, March 8, 2016

### Lecture 16

Today's lecture was about solving some problems using Pontryagin's Maximum.

We touched only briefly on controlled diffusion processes. The continuous time model for noise is Brownian motion, $B(t)$. The effect is that the Hamilton-Jacobi-Bellman equation gains another term, in $F_{xx}$.

I mentioned today in connection with section 16.1 (insects as optimizers) that zoologists have observed that in real-world choices of $t_{\text{switch}}$ some insect populations seem to do a good job of predicting $T$ (the time of the serious frost that ends the lives of the workers.) This example is from Luenberger, Introduction to Dynamic Systems, 1979, page 403.

## Thursday, March 3, 2016

### Lecture 15

The maximum principle is due to Lev Pontryagin. He was blind and yet one of the greatest mathematicians of his generation.

A key thing to grasp is that the PMP provides necessary conditions. We use the fact that an adjoint trajectory $\lambda$ must exist to deduce properties of, or completely determine, the optimal control and optimally controlled trajectory. To my thinking, the PMP is notoriously badly explained in most books. I hope I have been able to make it seem more intuitive.  Lagrange multipliers give a helpful interpretation, as does differentiation of the infinitestimal version of the optimality equation. We may also compare the PMP to the Lagrangian necessity theorem, which says that if $\phi(b)=f(x^*(b))=\max\{f(x):g(x)=b,x\in X\}$ is concave in $b$, then there exists a Lagrange multiplier $\lambda$ such that $f(x)+\lambda^T(b-g(x))$ is maximized with respect to $x\in X$ by $x^*(b)$.

For a crash course in Lagrangian optimization (a reminder of IB) you might like to look at pages 2-3 of these notes by Mike Tehranchi.

The rocket car example is a celebrated problem that was first solved by D.W. Bushaw, Differential Equations with a  Discontinuous Forcing Term, PhD Thesis, Princeton, 1952.

In the obituary of Donald W. Bushaw (1926-2012) it is stated that "Don’s PhD thesis is recognized as the beginning of modern optimal control theory."

There is a nice interactive demo of the solution to the rocket car parking problem that you can try.

Some personal experience of the power of PMP came in solving the problem in the paper

R. R. Weber. Optimal search for a randomly moving object. J. Appl. Prob. 23:708-717, 1986.

Here I used PMP to solve in continuous time the problem of searching for a moving object (Section 5.1). This is still an open problem in discrete time.

## Tuesday, March 1, 2016

### Lecture 14

On the basis of today's lecture you should be able to do Examples Sheet 3, #3, 4, 5. Questions 7--10 use Pontryagin's maximum principle.

I started the lecture with examples of two methods applied to solution of the continuous-time LQR problem.

Method 1 is the solution of this problem using the Hamilton Jacobi Bellman equation.

Method 2 foreshadows the use of Pontryagin's Maximum Principle, which we shall discuss in lectures 15, 16. You will need this for #6, 7, 8, 9, 10, which should all be possible after lecture 15.

In the fishing example we were essentially using Theorem 14.1 to show that an optimal policy is one for which,
$$u=\begin{cases} 0\\ a(\bar x)\\ u_{\max} \end{cases}\text{as }\ x\begin{array}{c} <\\ =\\ > \end{array}\bar x$$
and that a necessary condition is that $a'(\bar x)=\alpha$. On page 60 of the notes, $F(x)$ is computed at a function of $T$, the time at which $x$ first becomes equal to $\bar x$. We also check that $F_{xx}<0$, so $F$ is concave. All this information confirms that $F$ satisfies the HJB equation (14.2).

Notice that Figure 2 on page 60 is showing $u_{\max}\geq a(x)$ for all $x$, so fishing at maximum rate does cause the population to decrease (unless initially $a(x)=u_{\max}$).