one of the significant strategies of the power systems is economic dispatch (ed) problem, which is defined as the optimal generation of power units to produce energy at the lowest cost by fulfilling the demand within several limits. the undeniable impacts of ramp rate limits, valve loading, prohibited operating zone, spinning reserve and multi-fuel option on the economic dispatch of practical p...

We study solution approaches to a class of mixed-integer nonlinear programming problems that arise from recent developments in risk-averse stochastic optimization and contain second-order and p-order cone programming as special cases. We explore possible applications of some of the solution techniques that have been successfully used in mixed-integer conic programming and show how they can be g...

This paper presents a new recurrent neural network for solving convex nonlinear programming problems. The new model is simpler and more intuitive than existing models and converge very fast to exact solution of the original problem. We show that this new model is asymptotically stable. 2004 Elsevier Inc. All rights reserved.

In this paper a new neural network is proposed to solve nonlinear convex programming problems. The proposed neural network is shown to be asymptotically stable in the sense of Lyapunov. Comparing with the existing neural networks, the proposed neural network has fewer state variables and simpler architecture. Numerical examples show that the proposed network is feasible and efficient.

We propose various novel embedded approaches for (simultaneous) feature selection and classification within a general optimisation framework. In particular, we include linear and nonlinear SVMs. We apply difference of convex functions programming to solve our problems and present results for artificial and real-world data.

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 describe how to reformulate a problem that has second-order cone and/or semidefiniteness constraints in order to solve it using a general-purpose interior-point algorithm for nonlinear programming. The resulting problems are smooth and convex, and numerical results from the DIMACS Implementation Challenge problems and SDPLib are provided.

in this paper, an optimization problem with a linear objective function subject to a consistent finite system of fuzzy relation inequalities using the max-product composition is studied. since its feasible domain is non-convex, traditional linear programming methods cannot be applied to solve it. we study this problem and capture some special characteristics of its feasible domain and optimal s...

A wide range of problems arising in practical applications can be formulated as Mixed-Integer Nonlinear Programs (MINLPs). For the case in which the objective and constraint functions are convex, some quite effective exact and heuristic algorithms are available. When nonconvexities are present, however, things become much more difficult, since then even the continuous relaxation is a global opt...

In this paper, we present a novel sequential convex bilevel programming algorithm for the numerical solution of structured nonlinear min–max problemswhich arise in the context of semi-infinite programming. Here, our main motivation are nonlinear inequality constrained robust optimization problems. In the first part of the paper, we propose a conservative approximation strategy for such nonlinea...