Convexity theory and duality theory are important issues in math- ematical programming. Within the framework of credibility theory, this paper rst introduces the concept of convex fuzzy variables and some basic criteria. Furthermore, a convexity theorem for fuzzy chance constrained programming is proved by adding some convexity conditions on the objective and constraint functions. Finally,...

In this paper we explore the relations between the standard dual problem of a convex generalized fractional programming problem and the “partial” dual problem proposed by Barros et al. (1994). Taking into account the similarities between these dual problems and using basic duality results we propose a new algorithm to directly solve the standard dual of a convex generalized fractional programmi...

The problem regarding of optimal power flow in bipolar DC networks is addressed this paper from the recursive programming stand view. A hyperbolic relationship between constant terminals and voltage profiles used to resolve networks. proposed approximation based on Taylors’ Taylor series expansion. In addition, nonlinear relationships dispersed generators are relaxed small voltage-magnitude var...

Here we present a center-cut algorithm for convex mixed-integer nonlinear programming (MINLP) that can either be used as a primal heuristic or as a deterministic solution technique. Like many other algorithms for convex MINLP, the center-cut algorithm constructs a linear approximation of the original problem. The main idea of the algorithm is to use the linear approximation differently in order...

Convex Nonlinear Programming problems are all alike; every nonconvex problem is difficult in its own way. I am not the first numerical analyst to borrow the most quoted line of Anna Karenina to highlight the difficulties of non-linearity or non-convexity1. It could be argued that convex problems are not really “all alike”, but happy families are not either, therefore both the famous first sente...

An observer-based design method of fault-tolerant controller for uncertain linear systems subject to actuator faults, saturation and bounded disturbances is provided in this paper. A state feedback controller is designed to maximize the attraction domain with the state variables estimated by a Luenberger observer. The closed-loop system is modeled as a linear system with decentralized dead-zone...

This paper addresses the problem of generating cuts for mixed integer nonlinear programs where the objective is linear and the relations between the decision variables are described by convex functions defining a convex feasible region. We propose a new method for strengthening the continuous relaxations of such problems using cutting planes. Our method can be seen as a practical implementation...

We investigate some approaches to solving nonconvex global optimization problems by convex nonlinear programming methods. We assume that the problem becomes convex when selected variables are fixed. The selected variables must be discrete, or else discretized if they are continuous. We provide a survey of disjunctive programming with convex relaxations, logic-based outer approximation, and logi...

A variety of nonlinear, including semidefinite, relaxations have been developed in recent years for nonconvex optimization problems. Their potential can be realized only if they can be solved with sufficient speed and reliability. Unfortunately, state-of-the-art nonlinear programming codes are significantly slower and numerically unstable compared to linear programming software. In this paper, ...

In multi-objective convex optimization it is necessary to compute an infinite set of nondominated points. We propose a method for approximating the nondominated set of a multi-objective nonlinear programming problem, where the objective functions and the feasible set are convex. This method is an extension of Benson’s outer approximation algorithm for multi-objective linear programming problems...