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In the setting of Ben-Tal's generalized algebraic operations, this paper deals with Mond-Weir type dual theorems of multiobjective programming problems involving generalized invex functions. Two classes of functions, namely, (h,φ)-pseudoinvex and (h,φ)-quasi-invex, are defined for a vector function. By utilizing these two classes of functions, some dual theorems are established for conditionally proper efficient solution in (h,φ)-multiobjective programming problems. [ABSTRACT FROM AUTHOR]

We focus our study on a discussion of duality relationships of a minimax fractional programming problem with its two types of second-order dual models under the second-order generalized convexity type assumptions. Results obtained in this paper naturally unify and extend some previously known results on minimax fractional programming in the literature. [ABSTRACT FROM AUTHOR]

This paper is concerned with a pair of second-order mixed symmetric dual programs involving nondifferentiable functions. Weak, strong, and converse duality theorems are proved for afore-mentioned pair using the notion of second-order F-convexity/pseudoconvexity assumptions. [ABSTRACT FROM AUTHOR]

Feature scaling has attracted considerable attention during the past several decades because of its important role in feature selection. In this paper, a novel algorithm for learning scaling factors of features is proposed. It first assigns a nonnegative scaling factor to each feature of data and then adopts a generalized performance measure to learn the optimal scaling factors. It is of interest to note that the proposed model can be transformed into a convex optimization problem: second-order cone programming (SOCP). Thus the scaling factors of features in our method are globally optimal in some sense. Several experiments on simulated data, UCI data sets, and the gene data set are conducted to demonstrate that the proposed method is more effective than previous methods. [ABSTRACT FROM AUTHOR]

This paper proposes an improved geometric programming approach to address the optimization of biochemical systems. In the proposed method we take advantage of a special and interesting class of nonlinear kinetic models known as generalized mass action (GMA) models. In most situations optimization problems with GMA models are nonconvex and difficult problems to solve for global optimality. To deal with this difficulty, in this work, some transformation strategy is first used to convert the optimization problem with GMA models into an equivalent problem. Then a convexification technique is applied to transform this resulting optimization problem into a series of standard geometric programming problems that can be solved to reach a global solution. Two case studies are presented to demonstrate the advantages of the proposed method in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

A combined interior point homotopy continuation method is proposed for solving general multiobjective programming problem. We prove the existence and convergence of a smooth homotopy path from almost any interior initial interior point to a solution of the KKT system under some basic assumptions. [ABSTRACT FROM AUTHOR]

A class of G-semipreinvex functions, which are some generalizations of the semipreinvex functions, and the G-convex functions, is introduced. Examples are given to show their relations among G-semipreinvex functions, semipreinvex functions and G-convex functions. Some characterizations of G-semipreinvex functions are also obtained, and some optimality results are given for a class of G-semipreinvex functions. Ours results improve and generalize some known results. [ABSTRACT FROM AUTHOR]