Stage-oe-small.jpg

Article3026: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
(Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Stein |ErsterAutorVorname=Michael }} {{Publikation Author |Rank=2 |Author=Jürgen Branke }} {{Publikation Author |…“)
 
 
Zeile 12: Zeile 12:
 
}}
 
}}
 
{{Article
 
{{Article
|Referiert=False
+
|Referiert=True
 
|Title=Efficient implementation of an active set algorithm for large-scale portfolio selection
 
|Title=Efficient implementation of an active set algorithm for large-scale portfolio selection
 
|Year=2008
 
|Year=2008
Zeile 23: Zeile 23:
 
}}
 
}}
 
{{Publikation Details
 
{{Publikation Details
|Abstract=This paper deals with the efficient implementation of parametric quadratic programming that is specialized for large-scale mean-variance portfolio selection with a dense covariance matrix. The aim is to calculate the whole Pareto front of solutions that represent the trade-off between maximizing expected return and minimizing variance of return. We describe and compare in a uniform framework several techniques to speed up the necessary matrix operations, namely the initial matrix decomposition, the solution process in each iteration, and the matrix updates. Techniques considered include appropriate ordering of the matrix rows and columns, reducing the size of the system of linear equations, and dividing the system into two parts. Regarding implementation, we suggest to simultaneously use two different matrix representations that are specifically adapted to certain parts of the algorithm and propose a technique that prevents algorithm stalling due to numerical errors. Finally, we analyse and compare the runtime of these algorithm variants on a set of benchmark problems. As we demonstrate, the most sophisticated variant is several orders of magnitude faster than the standard implementation on all tested problem instances.  
+
|Abstract=This paper deals with the efficient implementation of parametric quadratic programming that is specialized for large-scale mean-variance portfolio selection with a dense covariance matrix. The aim is to calculate the whole Pareto front of solutions that represent the trade-off between maximizing expected return and minimizing variance of return. We describe and compare in a uniform framework several techniques to speed up the necessary matrix operations, namely the initial matrix decomposition, the solution process in each iteration, and the matrix updates. Techniques considered include appropriate ordering of the matrix rows and columns, reducing the size of the system of linear equations, and dividing the system into two parts. Regarding implementation, we suggest to simultaneously use two different matrix representations that are specifically adapted to certain parts of the algorithm and propose a technique that prevents algorithm stalling due to numerical errors. Finally, we analyse and compare the runtime of these algorithm variants on a set of benchmark problems. As we demonstrate, the most sophisticated variant is several orders of magnitude faster than the standard implementation on all tested problem instances.
 
|DOI Name=DOI=10.1016/j.cor.2007.05.004
 
|DOI Name=DOI=10.1016/j.cor.2007.05.004
 
|Projekt=P-Opt
 
|Projekt=P-Opt
 
|Forschungsgruppe=Effiziente Algorithmen
 
|Forschungsgruppe=Effiziente Algorithmen
 
}}
 
}}

Aktuelle Version vom 22. August 2010, 13:04 Uhr


Efficient implementation of an active set algorithm for large-scale portfolio selection


Efficient implementation of an active set algorithm for large-scale portfolio selection



Veröffentlicht: 2008 Dezember

Journal: Computers and Operations Research
Nummer: 12
Seiten: 3945-3961
Verlag: Elsevier
Volume: 35


Referierte Veröffentlichung

BibTeX




Kurzfassung
This paper deals with the efficient implementation of parametric quadratic programming that is specialized for large-scale mean-variance portfolio selection with a dense covariance matrix. The aim is to calculate the whole Pareto front of solutions that represent the trade-off between maximizing expected return and minimizing variance of return. We describe and compare in a uniform framework several techniques to speed up the necessary matrix operations, namely the initial matrix decomposition, the solution process in each iteration, and the matrix updates. Techniques considered include appropriate ordering of the matrix rows and columns, reducing the size of the system of linear equations, and dividing the system into two parts. Regarding implementation, we suggest to simultaneously use two different matrix representations that are specifically adapted to certain parts of the algorithm and propose a technique that prevents algorithm stalling due to numerical errors. Finally, we analyse and compare the runtime of these algorithm variants on a set of benchmark problems. As we demonstrate, the most sophisticated variant is several orders of magnitude faster than the standard implementation on all tested problem instances.

DOI Link: DOI=10.1016/j.cor.2007.05.004

Projekt

P-Opt



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet