Laura Martein

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The aim of the paper is to present sequential methods for a pseudoconvex optimization problem whose objective function is the sum of a linear and a linear fractional function and the feasible region is a polyhedron, not necessarily compact. Since the sum of a linear and a linear fractional function is not in general pseudoconvex, we first derive conditions(More)
We deal with a class of generalized fractional programming problems having a polyhedral feasible region and as objective the ratio of an affine function and the power p > 0 of an affine one. We aim to propose simplex-like sequential methods for finding the global maximum points. As the objective function may have local maximum points not global, we analyze(More)
We present significant numerical evidence, based on the entropy analysis by lumping of the binary expansion of certain values of the Gamma function, that some of these values correspond to incompressible algorithmic information. In particular, the value corresponds to a peak of non-compressibility as anticipated on a priori grounds from number-theoretic(More)
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