Postgraduate Course: Nonlinear Optimization (MATH11031)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||The solution of optimal decision-making and engineering design problems in which the objective and constraints are nonlinear functions of potentially (very) many variables is required on an everyday basis in the commercial and academic worlds. A closely-related subject is the solution of nonlinear systems of equations, also referred to as least-squares or data fitting problems that occur in almost every instance where observations or measurements are available for modelling a continuous process or phenomenon, such as in weather forecasting. The mathematical analysis of such problems and study of the classical methods for their solution are fundamental for understanding both practical methods of solution and the nature of the solution which may be obtained. Thus it imparts knowledge and insight into the optimal choice of available methods (software) or ability to develop such techniques for the practical problems at hand.
- To introduce the analysis of nonlinear optimization problems.
- To present the classical methods for solving nonlinear optimization problems with and without constraints, and nonlinear equations.
Linesearch and trust-region methods for unconstrained optimization problems (steepest descent, Newton's method); conjugate gradient method; linear and nonlinear least-squares. First- and second-order optimality conditions for constrained optimization problems; overview of methods for constrained problems (active-set methods, sequential quadratic programming, interior point methods, penalty methods, filter methods).
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 20%, Examination 80%
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Nonlinear Optimization (MATH11031)||2:00|
| Understanding the construction and main solution ideas for nonlinear optimization problems. Ability to assess the quality of available methods and solutions for such problems, as well as to potentially develop such optimization techniques and implementations.
|Course organiser||Dr Sergio Garcia Quiles
Tel: (0131 6)50 5038
|Course secretary||Mrs Frances Reid
Tel: (0131 6)50 4883
© Copyright 2016 The University of Edinburgh - 3 February 2017 4:43 am