Titlebook: Generalized Convexity and Generalized Monotonicity; Proceedings of the 6 Nicolas Hadjisavvas,Juan Enrique Martínez-Legaz,Je Conference proc

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Representation of a Polynomial Function as a Difference of Convex Polynomials, with an Applicationt least one global minimum. To deal with this problem, it is useful to have a convex difference representation of all the functions involved, as this allows for employing so-called d.c. optimization techniques..Procedures that permit the calculation of the polynomial convex difference representation

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Generalized Convexity for Unbounded Sets: The Enlarged Spacee, by adjoining to the ordinary n-dimensional space “improper points” defined by directions, and developed a theory of convexity in such a space. Later the second author introduced a new model for the enlarged space, suitable for applying tools from the theory of cones, and closely related to the mo

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A Note on Minty Variational Inequalities and Generalized Monotonicitylities. In particular, it is shown that the Minty variational inequality problem derived from a map . defined on a convex domain is solvable on any nonempty, compact, and convex subdomain if and only if . is properly quasimonotone.

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結(jié)果

https://doi.org/10.1007/978-3-7091-8284-0is. The objective is to deduce theoretical properties that permit to develop algorithms in order to derive efficient points for the analyzed problems. In particular, we study the application of properties to certain biobjective problems.

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Abner Louis Notkins,Michael B. A. Oldstoneallows for employing so-called d.c. optimization techniques..Procedures that permit the calculation of the polynomial convex difference representation of any polynomial are presented and analyzed here, and an application is made to a real problem whose functions are polynomials of degrees up to four.

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Minimization of the Sum of Several Linear Fractional Functionst approximate algorithm for rank-3 problems, generalized convex multiplicative programming approach and branch and bound algorithm using piecewise convex underestimating function. We will show that we are now able to obtain a globally optimal solution for up to rank-10 problems in a practical amount of time.

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