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Fractional programming by lower subdifferentiability techniques

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Abstract

The notion of lower subdifferentiability is applied to the analysis of convex fractional programming problems. In particular, duality results and optimality conditions are presented, and the applicability of a cutting-plane algorithm using lower subgradients is discussed. These methods are useful also in generalized fractional programming, where, in the linear case, the performance of the cutting-plane algorithm is compared with that of the most efficient version of the Dinkelbach method, which is based on the solution of a parametric linear programming problem.

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Author information

Authors and Affiliations

  1. Departamento de Matemática Económica, Financiera y Actuarial, Universidad de Barcelona, Barcelona, Spain

    M. Boncompte (Lecturer)

  2. Departamento de Matemática Aplicada y Análisis, Universidad de Barcelona, Barcelona, Spain

    J. E. Martínez-Legaz (Associate Professor)

Authors
  1. M. Boncompte
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  2. J. E. Martínez-Legaz
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Additional information

Communicated by O. L. Mangasarian

The authors wish to thank Mr. Jaume Timoneda for his help in the implementation of the numerical methods on the computer and the referees for valuable comments and suggestions; the present improved statement and proof of Proposition 2.1 is due to one of them. Financial support from the Dirección General de Investigación Científica y Técnica (DGICYT), under project PS89-0058, is gratefully acknowledged.

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Boncompte, M., Martínez-Legaz, J.E. Fractional programming by lower subdifferentiability techniques. J Optim Theory Appl 68, 95–116 (1991). https://doi.org/10.1007/BF00939937

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