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Department of Engineering
Dean:	Dr. Ármann Gylfason
Email:	ru@ru.is
Website:	http://www.ru.is/tvd
Teachers	View

MSc in Financial Engineering
Semesters:	4
Years:	2
ETCS:	120
About major	Fjármálaverkfræði er þverfagleg grein sem samþættir fjármálafræði við verkfræðilegar aðferðir, stærðfræðileg líkön, tölfræði, aðgerðagreiningu og hagnýta tölvunarfræði. Nám í fjármálaverkfræði hentar þeim sem hafa áhuga á t.d. áhættustýringu, afleiðuviðskiptum eða fjárstýringu. MSc námið er mótað að kröfum Verkfræðingafélags Íslands um fullnaðarmenntun í verkfræði.
Learning Outcomes	View
Education cycle	2

Legend
	Mandatory course on major		Teaching language
	Optional course on major		Prerequisites for course

Vorönn/Spring 2024

Energy Financial Assessment

SE-833-FA2

ECTS 6

Engineering Optimization

T-423-ENOP

ECTS 6

Sleep

T-424-SLEE

ECTS 6

Decision Analysis for Management

T-603-AKVA

ECTS 6

Internship in Engineering I

T-706-INT1

ECTS 6

Internship in Engineering II

T-706-INT2

ECTS 6

Systems Biology

T-765-SYBI

ECTS 8

Project Management and Strategic Planning

T-803-VERK

ECTS 8

Derivatives and risk management

T-814-DERI

ECTS 8

Creating a Complete Business Plan for a Technical Idea - Entrepreneurship and the Innovation Process

T-814-INNO

ECTS 8

Modern Trends in Financial Engineering

T-816-MTFE

ECTS 8

Tissue Engineering and Biomaterials

T-828-TISS

ECTS 8

Graduate Research Opportunities I

T-829-GRO1

ECTS 6

Finite Element Analysis in Engineering

T-844-FEMM

ECTS 8

Neural Engineering

T-863-NEUR

ECTS 8

Wind Power

T-863-WIND

ECTS 8

Power System Operation

T-867-POSY

ECTS 8

Stability and Control in Electric Power Systems

T-867-STAB

ECTS 8

Protection Philosophy for Smart-Grids

T-868-PROT

ECTS 8

MSc Thesis

T-899-MEIS

ECTS 30

MSc Thesis

T-900-MEIS

ECTS 30

MSc thesis II

T-901-MEI2

ECTS 30

Business Process Management

V-716-BPMA

ECTS 7,5

Entrepreneurial Finance

V-733-ENTR

ECTS 7,5

Business Intelligence and Analytics

V-784-REK5

ECTS 7,5

Entrepreneurship and Starting New Ventures

X-204-STOF

ECTS 6

Exchange Studies

X-699-EXCH

ECTS 30

Sumar/Summer 2024

Graduate Research Opportunities I

T-829-GRO1

ECTS 6

Exchange Studies

X-699-EXCH

ECTS 30

Haustönn/Fall 2024

Special Topics in Energy I

SE-801-STE

ECTS 1

Energy Economics

SE-805-EC1

ECTS 6

Special Topics in Energy III

SE-806-STE

ECTS 6

Embedded System Programming

T-738-EMBE

ECTS 8

Simulation II

T-806-SIMU

ECTS 6

Quality Management

T-807-QUAL

ECTS 6

Applying Models in Management

T-808-NOLI

ECTS 8

Data Mining and Machine Learning

T-809-DATA

ECTS 8

Optimization Methods

T-810-OPTI

ECTS 8

Year

1. year

Semester

Fall 2024

Level of course

4. Second cycle, introductory

Type of course

Elective

Prerequisites

T-403-ADGE, Operation Research

Schedule

No schedule found.

Lecturer

Eyjólfur Ingi Ásgeirsson

Content

Level of course: 3. Undergraduate (First cycle), advanced / 4. Graduate (Second cycle), introductory.
Type of course: Core in MSc Engineering Management and MSc Financial Engineering, elective in other programmes.
Mandatory prerequisites: Operation Research (T-403-ADGE).Overview and approach: This course introduces the principal algorithms for linear, network, discrete, nonlinear, dynamic optimization and optimal control. Emphasis is on methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton´s method, heuristic methods, and dynamic programming and optimal control methods.Reading material: Hillier and Lieberman, Introduction to Operations Research, 10th Edition, Pearson 2014.

Learning outcome - Objectives

After the completion of this course students will be capable of using basic methods of Operations Research for analysing and solving complex decision problems. More specifically the student will be able to:

Understand the properties of linear optimization and how it can be used to analyze and solve complex decision problems;
Use and analyze different forms of linear optimization models;
Understand and be capable of analyzing the geometry of linear optimization;
Apply systematic methods and algoirthms for analysing and solving decision problems;
Understand the importance and usefullness of linear optimization and its applications;
Apply software to solve optimization models;
Implement solution methods for linear optimization models and have in-depth understanding of the mechanics of the Simplex methods;
Practice the use of sensitivity analysis and to derive formulas for sensitivity of model parameters;
Understand integer programming and how it can used in decision making;
Use the main solution methods for integer programming;
Understand the special properties of network models and formulate practical problems as network models;
Understand the nature of non-linear optimization problems and the challenges involved in solving the problems;
Be familiar with different classes on non-linear optimization models and some of the available solution methods and algorithms;
Understand the importance of optimization under uncertainty and be able to develop robust programming, change constraints and stochastic programming models;
Be familiar with dynamic programming;
Present results in a clear and organized manner.