## Course information

This is a home page of information for Richard Weber's course of 16 lectures to third year Cambridge mathematics students in autumn 2014, starting October 9, 2014 (Tue/Thu @ 12 in CMS meeting room 4). This material is provided for students, supervisors (and others) to freely use in connection with this course. A web page for the winter 2013 course home page is also still available.

## Introduction

This is a course on optimization problems that are posed over time. Importantly we now add dynamic and stochastic elements that were not present in the IB Optimization course, and we consider what happens when some variables are imperfectly observed. Applications of this course are to be found in science, economics and engineering (e.g., "insects as optimizers", "planning for retirement", "finding a parking space", "optimal gambling" and "steering a space craft to the moon").

There are no absolute prerequisites, although acquaintance with Probability, Markov chains and Lagrangian multipliers is helpful. However,everything you need to know that cannot be picked up along the way. An important part of the course is to show you a range of applications. The course should appeal to those who liked the type of problems occurring in Optimization IB, Markov Chains IB and Probability IA.

The diagram of the trolley on wheels at the top of this page models the problem that a juggler has when she tries to balance a broom upright in one hand. In this course you will learn how to balance *N* brooms simultaneously, on top of one another, or perhaps side by side. Can you guess which of these two juggling trick is more difficult?

## Course notes

I aim to make each lecture a self-contained unit on a topic, with notes of four A4 pages.

Here are my starting course notes 2014. Initially, these are identical to the final version of the course notes 2013. But I will probably make some changes as the course proceeds. You can download a copy now, and take another copy once the course ends.

These notes include:

Table of Contents

Lectures 1 – 16

Slides used in Lecture 7: Multi-armed bandits and Gittins index

Index

The notes contain hyperlinks, but you may not see these in your browser. You will have to download a copy of the pdf file. If you click on an entry in the table of contents, or on a page number in the Index, you be taken to the appropriate page.

If you think you find a mistake in these notes, please check that you have the most recent copy (as I may have already made a correction.) Otherwise, please let me know and I will incorporate that correction into the notes.

There also exists a nice set of notes for this course by James Norris.

## Course Blog

There is a course blog in which I will make a few comments after each lecture: to emphasize an idea, give a sidebar, correction (!), or answer an interesting question (that perhaps a student sends to me in email).

## Example Sheets

There are 3 examples sheets, each containing 11 questions, in this single file: exsheetoc.pdf. You should receive a supervision on each examples sheet.

## Comments, Discussion and Questions

You can comment or ask questions on the discussion page, and also at the end of each of the blog posts. I will endeavour to answer questions. When you comment, you can do this anonymously if you wish. Just give any name and leave the email address box blank. You can also send me email with questions, suggestions or if you think you have discovered a mistake in the notes.

## Feedback

This year's course will finish on Tuesday 2 December 2014. The written feedback questionnaire has been completed by __ students, and a further __ have completed the electronic form. If you have not yet given feedback you can do so using my equivalent on-line form. This will be sent to my email anonymously. After reading the responses, I will forward them to the Faculty Office.

## Exam questions

Here is a file of all tripos questions in Optimization and Control for 2001-present.

Here are past tripos questions 1995-2006: tripos.pdf

Some of these questions have amusing real-life applications.

## Recommended books

You should be able these books in libraries around Cambridge. Clicking on the title link will show you where the book can be found.

**Bertsekas, D. P.**, Dynamic Programming and Optimal Control, Volumes I and II, Prentice Hall, 3rd edition 2005. (Useful for all parts of the course.) ISBN 1886529086

See also author's web page

**Hocking, L. M.**, Optimal Control: An introduction to the theory and applications, Oxford 1991. (Chapters 4-7 are good for Part III of the course.) ISBN 0198596820

**Ross, S.**, Introduction to Stochastic Dynamic Programming. Academic Press, 1995. (Chapters I-V are useful for Part I of the course.) ISBN 0125984219

**Whittle, P.**, Optimization Over Time. Volumes I and II. Wiley, 1982-83.(Chapters 5, 17 and 18 of volume I are useful for Part II of the course. Chapter 7, volume II is good for Part III of the course.) ISBN 0471101206

See also a nice set of notes for this course by James Norris.

## Schedules

**Dynamic programming**

The principle of optimality. The dynamic programming equation for ﬁnite-horizon problems. Interchange arguments. Markov decision processes in discrete time. Inﬁnite-horizon problems: positive, negative and discounted cases. Value interation. Policy improvment algorithm. Stopping problems. Average-cost programming. [6]

**LQG systems**

Linear dynamics, quadratic costs, Gaussian noise. The Riccati recursion. Controllability. Stabilizability. Inﬁnite-horizon LQ regulation. Observability. Imperfect state observation and the Kalman ﬁlter. Certainty equivalence control. [5]

**Continuous-time models**

The optimality equation in continuous time. Pontryagin’s maximum principle. Heuristic proof and connection with Lagrangian methods. Transversality conditions. Optimality equations for Markov jump processes and diﬀusion processes. [5]

## Contents

** 1 Dynamic Programming**

1.1 Control as optimization over time

1.2 The principle of optimality

1.3 Example: the shortest path problem

1.4 The optimality equation

1.5 Markov decision processes

** 2 Examples of Dynamic Programming**

2.1 Example: managing spending consumption and savings

2.2 Example: exercising a stock option

2.3 Example: secretary problem

** 3 Dynamic Programming over the Infinite Horizon**

3.1 Discounted costs

3.2 Example: job scheduling

3.3 The operator formulation of the optimality equation

3.4 The infinite-horizon case

3.5 The optimality equation in the infinite-horizon case

3.6 Example: selling an asset

3.7 Example: minimizing flow time on a single machine

** 4 Positive Programming**

4.1 Example: possible lack of an optimal policy.

4.2 Characterization of the optimal policy

4.3 Example: optimal gambling

4.4 Value iteration

4.5 Example: pharmaceutical trials

**5 Negative Programming**

5.1 Example: a partially observed MDP

5.2 Stationary policies

5.3 Characterization of the optimal policy

5.4 Optimal stopping over a finite horizon

5.5 Example: optimal parking

** 6 Optimal stopping problems**

6.1 Bruss's odds algorithm

6.2 Example: Stopping a random walk

6.3 Optimal stopping over the infinite horizon

6.4 Sequential Probability Ratio Test

6.5 Bandit processes

** 7 Bandit Processes and the Gittins Index**

7.1 Multi-armed bandit problem

7.2 The two-armed bandit

7.3 Gittins index theorem

7.4 Playing golf with many balls

7.5 Example: Weitzman's problem

7.6 *Calculation of the Gittins index*

7.7 *Forward induction policies*

7.8 *Proof of the Gittins index theorem*

** 8 Average-cost Programming**

8.1 Average-cost optimality equation

8.2 Example: admission control at a queue

8.3 Value iteration bounds

8.4 Policy improvement algorithm

**9 Continuous-time Markov Decision Processes**

9.1 Stochastic scheduling on parallel machines

9.2 Controlled Markov jump processes

9.3 Example: admission control at a queue

**10 LQ Regulation**

10.1 The LQ regulation problem

10.2 The Riccati recursion

10.3 White noise disturbances

10.4 LQ regulation in continuous-time

10.5 Linearization of nonlinear models

** 11 Controllability and Observability**

11.1 Controllability and Observability

11.2 Controllability

11.3 Controllability in continuous-time

11.4 Example: broom balancing

11.5 Stabilizability

11.6 Example: pendulum

11.7 Example: satellite in a plane orbit

**12 Observability and the LQG Model**

12.1 Infinite horizon limits

12.2 Observability

12.3 Observability in continuous-time

12.4 Example: satellite in planar orbit

12.5 Imperfect state observation with noise

** 13 Kalman Filter and Certainty Equivalence**

13.1 The Kalman filter

13.2 Certainty equivalence

13.3 The Hamilton-Jacobi-Bellman equation

13.4 Example: LQ regulation

**14 Pontryagin's Maximum Principle**

14.1 Example: parking a rocket car

14.2 Heuristic derivation of Pontryagin's maximum principle

14.3 Example: parking a rocket car (continued)

14.4 Adjoint variables as Lagrange multipliers

**15 Uses of Pontryagin's Maximum Principle**

15.1 Transversality conditions

15.2 Example: use of transversality conditions

15.3 Problems in which time appears

15.4 Example: monopolist

15.5 Example: insects as optimizers

15.6 Problems with terminal conditions

15.7 Neoclassical economic growth

**16 Controlled Diffusion Processes**

16.1 The dynamic programming equation

16.2 Diffusion processes and controlled diffusion processes

16.3 Example: noisy LQ regulation in continuous time

16.4 Example: passage to a stopping set

Other resources and further reading

Here is what the Occupational Outlook Handbook says about careers in operations research.

Here is the home page of INFORMS, the Institute for Operations Research and the Management Sciences.

Here is the home page of the UK Operational Research Society.