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    Equivalence in linear fractional programming

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    In this paper two algorithms are suggested for solving a linear fractional problem whatever the feasible region is. Such algorithms can be interpreted as a modified version of Martos and Charnes-Cooper algorithms. Successively, it will be shown that the two methods are algorithmically equivalent in the sense that they generate the same finite sequence of points leading to an optimal solution. This last result can be viewed as an extension of the one given by Wagner-Yuan for a compact feasible region

    On the maximal domains of pseudoconvexity of a quadratic fractional function

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    In this paper we will characterize the maximal domains of pseudoconvexity of the ratio betwcen a quadratic function and an affine one. Furthermore, motivated by thc fact that in optimization problems the decision variables are often required to be nonnegative, we will specialize the obtained results in order to achieve conditions which guarantee that the nonnegative orthant is contained in thc maximal domains of pseudoconvexity of the function

    Generalized Convexity and Optimality Conditions in Scalar and Vector Optimization

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    In this chapter the role of generalized convex functions in optimization is stressed. A particular attention is devoted to local-global properties, to optimality of stationary points and to sufficiency of first order necessary optimality conditions for scalar and vector problems. Desite of the numerous classes of generalized convex functions suggested in these last fifty years, we have limited ourselves to introduce and stydy those classes of scalar and vector functions which are more used in the literature
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