A New Algorithm for Generalized Franctional Programs

B.A. Frenck, J.B. Schaible and S. Zhang

The A. Gary Anderson Graduate School of Management from The A. Gary Anderson Graduate School of Management. University of California Riverside

Abstract: A dual problem for convex generalized fractional programs with no duality gap is presented and it is shown how this dual program can be efficiently solved using a parametric approach. The resulting algorithm can be seen as "dual" to the Dinkelbach-type algorithm for generalized fractional programs since it approximates the optimal objective value of the dual (primal) problem from below. Convergence results for this algorithm are derived and easy condition to achieve superlinear convergence is also established. Moreover, under some additional assumptions the algorithm also recovers at the same time an optimal solution of the primal problem. We also consider a variant of this new algorithm, based on scaling the dual parametric function.

Keywords: PROGRAMMING; MATHEMATICS (search for similar items in EconPapers)
JEL-codes: C60 C61 (search for similar items in EconPapers)
Pages: 30 pages
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:fth:caland:96-02

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