The questions how to maximise your gain (or to minimise the cost) and how to determine the optimal strategy/policy are fundamental for an engineer, an economist, a doctor designing a cancer therapy, or a government planning some social policies. Many problems in mechanics, physics, neuroscience and biology can be formulated as optimistion problems. Therefore, optimisation theory is an indispensable tool for an applied mathematician. Optimisation theory has many diverse applications and requires a wide range of tools but there are only a few ideas underpinning all this diversity of methods and applications. This course will focus on two of them. We will learn how the concept of convexity and the concept of dynamic programming provide a unified approach to a large number of seemingly unrelated problems. By completing this unit you will learn how to formulate optimisation problems that arise in science, economics and engineering and to use the concepts of convexity and the dynamic programming principle to solve straight forward examples of such problems. You will also learn about important classes of optimisation problems arising in finance, economics, engineering and insurance.
Lecture 3hours/week, tutorial 1hr/week
Assignment (15%), assignment (15%), exam (70%)
MATH2X21 and MATH2X23 and STAT2X11
[A mark of 65 or greater in 12cp from (MATH2070 or MATH2970 or STAT2011 or STAT2911 or MATH2021 or MATH2921 or MATH2022 or MATH2922 or MATH2023 or MATH2923 or MATH2061 or MATH2961 or MATH2065 or MATH2965 or MATH2962 or STAT2012 or STAT2912 or DATA2002 or DATA2902)] or [12 cp from (MATH3075 or MATH3975 or STAT3021 or STAT3011 or STAT3911 or STAT3888 or STAT3014 or STAT3914 or MATH3063 or MATH3963 or MATH3061 or MATH3961 or MATH3962 or MATH3963 or MATH3969 or MATH3974 or MATH3976 or MATH3977 or MATH3978 or MATH3979)]Prohibitions