Resolution Methods

Algorithms for Solving Financial Portfolio Design Problems - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-7998-1882-3.ch002 ◽

2020 ◽

pp. 29-60

Keyword(s):

Linear Programming ◽

Simulated Annealing ◽

Tabu Search ◽

Symmetry Breaking ◽

Constraint Programming ◽

Design Problem ◽

Variable Neighborhood Search ◽

Neighborhood Search ◽

Performance Profiles ◽

Portfolio Design

The aim of this chapter is to introduce the different notions of the techniques used to solve the portfolio design problem. These techniques can be divided into two exact (or complete) methods and approached (or incomplete) methods. In the first part, the authors provide the exact approaches, namely linear programming and constraint programming, as well as the techniques of symmetry breaking, the modeling notions, and the different solving algorithms. The second part concerns approached methods, namely Simulated Annealing, IDWalk, Tabu Search, GWW, and Variable Neighborhood Search, including the techniques of studying the performance profiles of a method.

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Global Results Synthesis

Algorithms for Solving Financial Portfolio Design Problems - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-7998-1882-3.ch008 ◽

2020 ◽

pp. 165-175

Keyword(s):

Linear Programming ◽

Simulated Annealing ◽

Tabu Search ◽

Local Search ◽

Constraint Programming ◽

Cpu Time ◽

Exact Approach ◽

First Results ◽

Programming Techniques ◽

Portfolio Design

This chapter provides a global synthesis of the realized results by applying exact and approximate approaches on the portfolio design (PD) problem. The authors introduce an experimental analysis of best approaches based on linear programming and constraint programming techniques, according to the CPU time. Next, a global experiment synthesis of the best approximate approaches based on Simulated Annealing, IDWalk, Tabu Search, GWW, and VNS is realized according to the number of success and the CPU time. First results show that constraint programming with breaking all the detected symmetries is the best as an exact approach, VNS combined with simulated annealing is effective on non-trivial instances of the problem, and simulated annealing is the most effective as a simple local search.

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Constraint Programming Applied to Portfolio Design Problem

Algorithms for Solving Financial Portfolio Design Problems - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-7998-1882-3.ch005 ◽

2020 ◽

pp. 103-118

Keyword(s):

Symmetry Breaking ◽

Constraint Programming ◽

Design Problem ◽

Experimental Results ◽

Complex Problems ◽

Portfolio Design

This chapter introduces Constraint Programming (CP) approaches for solving efficiently a ðnancial portfolio design problem. The CP includes powerful techniques for modeling and solving complex problems. Symmetry breaking coming firstly from CP has proved its efficiency in minimizing CPU times when the problem is symmetric. The authors have adopted CP techniques to model the problem in a constraints system to capitalize on the flexibility of the CP paradigm and to take into consideration the symmetric aspect of the problem. The authors propose different CP models and different hybridizations of symmetry breaking techniques to tackle the problem. Experimental results on non-trivial instances of the problem show the effectiveness of the CP approach.

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Algoritmos metaheurísticos trayectoriales para optimizar problemas combinatorios

Informes Científicos - Técnicos UNPA ◽

10.22305/ict-unpa.v8i3.222 ◽

2016 ◽

Vol 8 (3) ◽

pp. 56-75

Author(s):

Natalia Alancay ◽

Silvia Villagra ◽

Norma Andrea Villagra

Keyword(s):

Simulated Annealing ◽

Tabu Search ◽

Variable Neighborhood Search ◽

Neighborhood Search

La aplicación de los algoritmos metaheurísticos a problemas de optimización ha sido muy importante durante las últimas décadas. La principal ventaja de estas técnicas es su flexibilidad y robustez, lo que permite aplicarlas a un amplio conjunto de problemas. En este trabajo nos concentramos en metaheurísticas basadas en trayectoria Simulated Annealing, Tabu Search y Variable Neighborhood Search cuya principal característica es que parten de un punto y mediante la exploración del vecindario varían la solución actual, formando una trayectoria. Mediante las instancias de los problemas combinatorios seleccionados, se realiza una experimentación computacional que ilustra el comportamiento de los métodos algorítmicos para resolver los mismos. El objetivo principal de este trabajo es realizar el estudio y comparación de los resultados obtenidos para las metaheurísticas trayectoriales seleccionadas en su aplicación para la resolución de un conjunto de problemas académicos de optimización combinatoria.

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