Course Details in

Module Title	LM Conic Optimization
School	Mathematics
Department	Mathematics
Module Code	06 23557
Module Lead	Professor Michal Kocvara
Level	Masters Level
Credits	10
Semester	Semester 1
Pre-requisites
Co-requisites
Restrictions	Prohibited combinations: 06 02444
Exclusions
Description	Many decision problems arising in managerial decision making in the public as well as in the private sector are inherently nonlinear, and the same holds for various problems occurring in science and engineering. Many of these nonlinear optimization problems can be formulated as convex conic programming problems, with different choices of the cone. In this course, the essential ideas as well as the most important classes of conic programming, second-order and semidefinite, are studied in detail. Applications of conic programming, for instance in robust optimization and eigenvalue optimization are also studied.
Learning Outcomes	By the end of the module, the student should be able to; understand and explain basic concept of conic programming; formulate optimisation problems as conic programs; formulate dual problems to different conic problems.
Assessment	23557-01 : Raw Module Mark : Coursework (100%)
Assessment Methods & Exceptions	One 90 minute formal written examination in University examination period (90%); Homework (10%)
Other	None
Reading List