ELEC97062 OptimisationLecturer(s): Prof Alessandro Astolfi Aims
The aim of this module is to equip you with the tools to formulate and solve general constrained and unconstrained optimisation problems. The module covers several introductory topics in optimisation such as necessary and sufficient conditions of optimality, basic optimization algorithms (gradient, Newton, conjugate directions, quasiNewton), KuhnTucker conditions, penalty method, recursive quadratic programming, and global optimization. Each topic is covered in a mathematical rigorous way with attention to regularity, convergence conditions, and complexity. The module assumes prior basic calculus and linear algebra knowledge such as multivariable calculus, sequences, compactness, and eigenvalues.
Learning Outcomes
Upon successful completion of this module, you will be able to: Formulate simple unconstrained and constrained optimization problems Classify optimal solutions Apply the correct methods to solve such problems Write basic unconstrained optimization algorithms and assess their convergence and numerical properties Apply the notion of penalty in the solution of constrained optimization problems Change constrained optimization problems into equivalent unconstrained problems Apply basic algorithms for the solutions of global optimization problems
Syllabus
Necessary and sufficient conditions of optimality Line search The gradient method, Newton's method, conjugate direction methods, quasiNewton methods, methods without derivatives KuhnTucker conditions Penalty function methods Exact methods Recursive quadratic programming Global optimization
Exam Duration: 3:00hrs Coursework contribution: 0% Term: Autumn Closed or Open Book (end of year exam): Open Coursework Requirement: To be announced Oral Exam Required (as final assessment): N/A Prerequisite module(s): None required Course Homepage: http://www3.imperial.ac.uk/people/a.astolfi/teaching/optimisation Book List: Please see Module Reading list
