Credits: 5

Schedule: 25.02.2019 - 09.04.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

IV Spring (2018-2019, 2019-2020)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

Building of optimization models, basic theory and main algorithms.

Content (valid 01.08.2018-31.07.2020): 

An introductory course to linear and nonlinear optimization. The following topics are included: Building of optimization models, resource allocation models, least-squares problems, goal programming, integer optimization and traveling salesman problem together with genetic algorithms. In exercises Excel and Matlab are used to solve the problems.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Interactive participation to the course, or exam.

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 

The 2019 edition of the course will have an assessment an exam at the end of the period. 

The students can also take weekly online quizzes that will supplement the exam grade. The quizzes are also to exercise the content and provide feedback to the students concerning their progress. 

Workload (valid 01.08.2018-31.07.2020): 

Contact hours 48h, attendance is not obligatory
Voluntary homework 10h
Autonomous studies 30h

Study Material (valid 01.08.2018-31.07.2020): 

Lecture notes. H. A. Taha: Operations Research, An Introduction , Prentice-Hall International

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Mat-2.2105 Introduction to Optimization

Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

MS-A00XX Matrix Algebra, MS-A01XX Differential and integral calculus 1, and MS-A01XX Differential and integral calculus 2.

Grading Scale (valid 01.08.2018-31.07.2020): 


Details on the schedule (applies in this implementation): 

Lec1Introduction + Formulation
Lec2Formulation + Graphical method
Lec3Simplex method
Lec4Simplex method - special cases
Lec5Linear duality + sensitivity analysis
Lec6Integer programming - formulation
Lec7Integer programming - B&B + cutting planes
Lec8Analysis - Convexity, unconstrained opt conditions
Lec9Constrained opt: KKT conditions
Lec10Line search, Gradient and Newton
Lec11Constr. Newton and Int. Point.


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