We provide Completely Positive and Copositive Optimization formulations for the Constrained Fractional Quadratic Problem (CFQP) and Standard Fractional Quadratic Problem (StFQP). Based on these formulations, Semidefinite Programming (SDP) relaxations are derived for finding good lower bounds to these fractional programs, which can be used in a global optimization branch-and-bound approach. Appl...

Abstract The focus in this paper is interior-point methods for bound-constrained nonlinear optimization, where the system of equations that arise are solved with Newton’s method. There a trade-off between solving Newton systems directly, which give high quality solutions, and many approximate computationally less expensive but lower solutions. We propose partial full solutions to systems. speci...

Two trust-region methods for systems of mixed nonlinear equalities, general inequalities and simple bounds are proposed. The first method is based on a Gauss-Newton model, the second one is based on a regularized Gauss-Newton model and results to be a Levenberg-Marquardt method. The globalization strategy uses affine scaling matrices arising in bound-constrained optimization. Global convergence...

We present in this article a new strategy for selecting the current node in an interval Branch and Bound algorithm for constrained global optimization. The standard best-first strategy selects the node with the lowest lower bound of the objective estimate. We propose in this article new node selection policies where an upper bound of each node/box is also taken into account. The good accuracy o...

In recent years, Particle Swarm Optimization (PSO) methods have gained popularity in solving single objective and other optimization tasks. In particular, solving constrained optimization problems using swarm methods has been attempted in past but arguably stays as one of the challenging issues. A commonly encountered situation is one in which constraints manifest themselves in form of variable...

Abstract. In Akrotirianakis and Floudas (2004) we presented the theoretical foundations of a new class of convex underestimators for C2 nonconvex functions. In this paper, we present computational experience with those underestimators incorporated within a Branch-and-Bound algorithm for box-conatrained problems. The algorithm can be used to solve global optimization problems that involve C2 fun...

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. M...

Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired b...

abstract it is the purpose of this article to introduce a linear approximation technique for solving a fractional chance constrained programming (cc) problem. for this purpose, a fuzzy goal programming model of the equivalent deterministic form of the fractional chance constrained programming is provided and then the process of defuzzification and linearization of the problem is started. a samp...

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...