# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014 -- ARCHIVE as at 1 September 2013 for reference onlyTHIS PAGE IS OUT OF DATE

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# Postgraduate Course: Nonlinear Optimization (MATH11031)

 School School of Mathematics College College of Science and Engineering Course type Standard Availability Available to all students Credit level (Normal year taken) SCQF Level 11 (Postgraduate) Credits 10 Home subject area Mathematics Other subject area Operational Research Course website http://student.maths.ed.ac.uk Taught in Gaelic? No Course description 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. Aims : - 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. Syllabus summary: 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).
 Pre-requisites Co-requisites Prohibited Combinations Other requirements None Additional Costs None
 Pre-requisites None Displayed in Visiting Students Prospectus? Yes
 Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  Yes Quota:  None Web Timetable Web Timetable Course Start Date 15/01/2014 Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 69 ) Additional Notes Breakdown of Assessment Methods (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 % No Exam Information
 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.
 See 'Breakdown of Assessment Methods' and 'Additional Notes' above.
 None
 Academic description Not entered Syllabus Not entered Transferable skills Not entered Reading list Not entered Study Abroad Not entered Study Pattern Not entered Keywords NO
 Course organiser Dr Julian Hall Tel: (0131 6)50 5075 Email: J.A.J.Hall@ed.ac.uk Course secretary Mrs Frances Reid Tel: (0131 6)50 4883 Email: f.c.reid@ed.ac.uk
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