Non Linear Programming (ΣΕΕ7)

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General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Graduate
Course Code	ΣΣΕ7
Semester	2
Course Title	Non Linear Programing
Independent Teaching Activities	Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type	Special Background
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes	The course aims to introduce students to the fundamentals of non-linear optimization. Upon successful completion of the course the student will be able to: understand the basic principles of nonlinear optimization problems. use some of the commonly used algorithms for nonlinear optimization (unconstrained and constrained). select the appropriate algorithm for a particular optimization problem.
General Competences	Working independently Decision-making Adapting to new situations Production of free, creative and inductive thinking Synthesis of data and information, with the use of the necessary technology

Syllabus

Introduction to unconstrained and constrained optimization, Lagrange Multipliers, Karush-Kuhn-Tucker conditions, Line Search, Trust Region, Conjugate Gradient, Newton, Quasi-Newton methods, Quadratic Programming, Penalty Barrier and Augmented Lagrangian Methods.

Teaching and Learning Methods - Evaluation

Delivery	Face-to-face
Use of Information and Communications Technology	Lindo/Lingo Software, Mathematica, Email, class web
Teaching Methods	Activity Semester Workload Lectures 39 Study and analysis of bibliography 78 Preparation of assignments and interactive teaching 70.5 Course total 187.5
Student Performance Evaluation	LANGUAGE OF EVALUATION: Greek METHODS OF EVALUATION: Written work (30%), Final exam (70%).

Attached Bibliography

• Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis. 3rd Edition. Wiley.
• Fang, K.T., and Zhang, Y.T.. (1990). Generalized Multivariate Analysis. Springer. Berlin.
• Flury, B. (1997). A first course in multivariate statistics. Springer.
• Johnson, R. A. and Wichern, D. W. (2006). Applied Multivariate Statistical Analysis. Prentice Hall.
• Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. Academic Press.
• Muirhead, R. J. (1982). Aspects of Multivariate Statistical Theory. Wiley.
• Rencher, A. C. (1995). Methods of Multivariate Analysis. Wiley.
• Srivastava, M. S. (2002). Methods of multivariate statistics. Wiley.