Research Seminar: Optimization

Class Objectives (Learning Outcomes)

After completion of the course, attendants will be able to:

model a real-world decision problem as a computer-solvable optimization problem (see below for basic literature),
employ optimization tools and methods in various fields (with a focus on Finance and Engineering),
develop extensions to the R optimization infrastructure (ROI) package, i.e. create so-called solver plugin packages,
create R package vignettes describing methods and tools implemented/developed throughout the course.

Prerequisites

Good knowledge of the R language, mathematics, and basics in optimization.

Teaching and Learning Methods

Reading seminar and R-based mini labs.

Contact

Instructor: Kurt Hornik (Kurt.Hornik _AT_ wu.ac.at)

Assistants: Ronald Hochreiter (Ronald.Hochreiter _AT_ wu.ac.at) and Stefan Theussl (Stefan.Theussl _AT_ wu.ac.at)

Schedule

Unit Date Time Topic Slides*

1 04.03. 09:00 -- 12:00 Preliminary Talk --

2 18.03. 09:00 -- 12:00 Stochastic Programming, ROI (Ronald, Stefan T.) [ROI]

3 13.05. 09:00 -- 12:00 Optimization Methods in Finance I (Kathrin, Maria) [pdf, R, dat1, dat2]

4 13.05. 13:00 -- 16:00 Optimization Methods in Finance II (Florian, Stefan K.) [pdf, R]

5 27.05. 09:00 -- 12:00 Sparse PCA (Norbert, Thomas) [pdf1, pdf2, R1, R2]

6 17.06. 09:00 -- 12:00 Kernel-based learning (Paul) [pdf]

Unit	Date	Time	Topic	Slides*
1	04.03.	09:00 -- 12:00	Preliminary Talk	--
2	18.03.	09:00 -- 12:00	Stochastic Programming, ROI (Ronald, Stefan T.)	[ROI]
3	13.05.	09:00 -- 12:00	Optimization Methods in Finance I (Kathrin, Maria)	[pdf, R, dat1, dat2]
4	13.05.	13:00 -- 16:00	Optimization Methods in Finance II (Florian, Stefan K.)	[pdf, R]
5	27.05.	09:00 -- 12:00	Sparse PCA (Norbert, Thomas)	[pdf1, pdf2, R1, R2]
6	17.06.	09:00 -- 12:00	Kernel-based learning (Paul)	[pdf]

* Slides, notes, and R code snippets are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

The seminar will take place at the Besprechungsraum of the Institute for Statistics and Mathematics, UZA2, Ebene 4.

Projects

Participants should pick one project out of the following list.

Kernel-based Learning
Optimization Methods in Finance I (Chapters 7,8,11,12)
Optimization Methods in Finance II (Chapters 5,6,13,14,15)
ROI
Sparse PCA

A package vignette (eight to twelve pages) is to be delivered at the end of the project.

Criteria for Successful Completion

Presentation of the project and package vignette.

Literature

S.P. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
G. Cornuejols and R. Tütüncü. Optimization Methods in Finance. Cambridge University Press, 2007
Robert Fourer, David M. Gay, and Brian W. Kernighan. The AMPL Book. Brooks/Cole Publishing Company, 2002.
Stefan Theussl. CRAN task view: Optimization and mathematical programming, 2010. link