DC programming and DCA for globally solving the value-at-risk

Dinh, Tao Pham; Nam, Nguyen Canh; Thi, Hoai An Le

doi:10.1007/s10287-009-0099-2

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…In this section, we present briefly the DC programming approach developed for solving nonconvex problems. For more details, see [23,26,27]. And we use DCA for solving problem (8).…”

Section: The Solution Methodsmentioning

confidence: 99%

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A novel approach for solving stochastic problems with multiple objective functions

Kasri

Bellahcene

2021

RAIRO-Oper. Res.

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In this paper we suggest an approach for solving a multiobjective stochastic linear programming problem with normal multivariate distributions. Our solution method is a combination between the multiobjective approach and a nonconvex technique. The problem is first transformed into a deterministic multiobjective problem introducing the expected value criterion and an utility function that represents the decision makers’ preferences. The obtained problem is reduced to a mono-objective quadratic problem using a weighting method. This last problem is solved by DC programming and DC algorithm. A numerical example is included for illustration.

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“…In this section, we present briefly the DC programming approach developed for solving nonconvex problems. For more details, see [23,26,27]. And we use DCA for solving problem (8).…”

Section: The Solution Methodsmentioning

confidence: 99%

“…The DC programming and DC Algorithm have been introduced by Pham Dinh Tao in their preliminary form in 1985 and developed by Le Thi and Pham Dinh since [19][20][21][22]. This method has proved its efficiency in a large number of nonconvex problems [23,26,27].…”

Section: Introductionmentioning

confidence: 99%

A novel approach for solving stochastic problems with multiple objective functions

Kasri

Bellahcene

2021

RAIRO-Oper. Res.

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“…Numerical experiments show that the DCA is more robust and efficient than standard methods (see e.g. [6][7][8][9]11,13,[15][16][17][18]20, and references therein]).…”

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confidence: 99%

Robust investment strategies with discrete asset choice constraints using DC programming

Gülpınar

Thi²,

Moeini³

2010

Optimization

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In this article, we are concerned with robust investment strategies for the portfolio management problem. We extend the classical Markowitz framework with discrete asset choice constraints to worst-case portfolio selection with rival risk and return scenario specifications. Robustness is ensured by considering the optimal strategy in view of multiple rival scenarios and evaluating the portfolio simultaneously with the worst-case scenario. Discrete constraints, such as buy-in thresholds and cardinality, represent the investor's choice on the assets. Portfolio allocation with discrete asset choice constraints is a non-convex and NP-hard problem. A local deterministic optimization approach based on difference of convex (DC) functions programming is introduced and a DC algorithm (DCA) is developed to solve min-max mean-variance portfolio optimization problem. The computational results using historical data show that the DCA is more efficient than the standard methods and often provides a global solution.

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A DC Algorithm for Solving Multiobjective Stochatic Problem via Exponential Utility Functions

Kasri

Bellahcene

2019

Advances in Intelligent Systems and Computing

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DC programming and DCA for globally solving the value-at-risk

Abstract: Value-at-risk, Bilevel linear programming, DC programming, DCA, Branch- and -bound,

Cited by 8 publications

References 13 publications

A novel approach for solving stochastic problems with multiple objective functions

A novel approach for solving stochastic problems with multiple objective functions

Robust investment strategies with discrete asset choice constraints using DC programming

A DC Algorithm for Solving Multiobjective Stochatic Problem via Exponential Utility Functions

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