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Wyszukujesz frazę "programming method" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
A continuous-time distributed algorithm for solving a class of decomposable nonconvex quadratic programming
Autorzy:
Zhao, Y.
Liu, Q.
Powiązania:
https://bibliotekanauki.pl/articles/91832.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
decomposable nonconvex quadratic programming
multi-agent network
consensus
Lyapunov method
Opis:
In this paper, a continuous-time distributed algorithm is presented to solve a class of decomposable quadratic programming problems. In the quadratic programming, even if the objective function is nonconvex, the algorithm can still perform well under an extra condition combining with the objective, constraint and coupling matrices. Inspired by recent advances in distributed optimization, the proposed continuous-time algorithm described by multi-agent network with consensus is designed and analyzed. In the network, each agent only accesses the local information of its own and from its neighbors, then all the agents in a connected network cooperatively find the optimal solution with consensus.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 4; 283-291
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Metaheuristic optimization of marginal risk constrained long - short portfolios
Autorzy:
Vijayalakshmi Pai, G. A.
Michel, T.
Powiązania:
https://bibliotekanauki.pl/articles/91858.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
metaheuristic
optimization
portfolio optimization
marginal risk
quadratic programming
meta heuristic method
data envelopment analysis
Opis:
The problem of portfolio optimization with its twin objectives of maximizing expected portfolio return and minimizing portfolio risk renders itself difficult for direct solving using traditional methods when constraints reflective of investor preferences, risk management and market conditions are imposed on the underlying mathematical model. Marginal risk that represents the risk contributed by an asset to the total portfolio risk is an important criterion during portfolio selection and risk management. However, the inclusion of the constraint turns the problem model into a notorious non-convex quadratic constrained quadratic programming problem that seeks acceptable solutions using metaheuristic methods. In this work, two metaheuristic methods, viz., Evolution Strategy with Hall of Fame and Differential Evolution (rand/1/bin) with Hall of Fame have been evolved to solve the complex problem and compare the quality of the solutions obtained. The experimental studies have been undertaken on the Bombay Stock Exchange (BSE200) data set for the period March 1999-March 2009. The efficiency of the portfolios obtained by the two metaheuristic methods have been analyzed using Data Envelopment Analysis.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 3; 259-274
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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