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Improving the Approximated Projected Perspective Reformulation by dual information

Frangioni, Antonio; Furini, Fabio; Gentile, Claudio (2017), Improving the Approximated Projected Perspective Reformulation by dual information, Operations Research Letters, 45, 5, p. 519-524. 10.1016/j.orl.2017.08.001

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Type
Article accepté pour publication ou publié
Date
2017
Journal name
Operations Research Letters
Volume
45
Number
5
Publisher
Elsevier
Pages
519-524
Publication identifier
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Author(s)
Frangioni, Antonio
Department of Computer Science [Pisa]
Furini, Fabio
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Gentile, Claudio
Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti"
Abstract (EN)
We propose an improvement of the Approximated Projected Perspective Reformulation (AP2R)of [1] for the case in which constraints linking the binary variables exist. The new approach requires to solve the Perspective Reformulation (PR) once, and then use the corresponding dual information to reformulate the problem prior to applying AP2R, there by combining the root bound quality of the PR with the reduced relaxation computing time of AP2R. Computational results for the cardinality-constrained Mean-Variance portfolio optimization problem show that the new approach is competitivewith state-of-the-art ones.
Subjects / Keywords
Mixed-Integer Non-Linear Problems; Semi-continuous variables; Perspective reformulation; Projection; Lagrangian relaxation; Portfolio optimization