In this paper, we present a new approach for solving absolute value equation (AVE) whichuse Levenberg-Marquardt method with conjugate subgradient structure. In conjugate subgradientmethods the new direction obtain by combining steepest descent direction and the previous di-rection which may not lead to good numerical results. Therefore, we replace the steepest descentdir...

Recently, important contributions on convergence studies of conjugate gradient methods have been made by Gilbert and Nocedal [6]. They introduce a “sufficient descent condition” to establish global convergence results, whereas this condition is not needed in the convergence analyses of Newton and quasi-Newton methods, [6] hints that the sufficient descent condition, which was enforced by their ...

The Particle Swarm Optimiser (PSO) is a population based stochastic optimisation algorithm, empirically shown to be efficient and robust. This paper provides a proof to show that the original PSO does not have guaranteed convergence to a local optimum. A flaw in the original PSO is identified which causes stagnation of the swarm. Correction of this flaw results in a PSO algorithm with guarantee...

The Global Health 2035 report notes that the "grand convergence"--closure of the infectious, maternal, and child mortality gap between rich and poor countries--is dependent on research and development (R&D) of new drugs, vaccines, diagnostics, and other health tools. However, this convergence (and the R&D underpinning it) will first require an even more fundamental convergence of the different ...

In this paper, we discuss dynamics of Cohen–Grossberg neural networks with discontinuous activations functions. We provide a relax set of sufficient conditions based on the concept of Lyapunov diagonally stability (LDS) for Cohen–Grossberg networks to be absolutely stable. Moreover, under certain conditions we prove that the system is exponentially stable globally or convergent globally in fini...

A novel inexact smoothing method is presented for solving the second-order cone complementarity problems (SOCCP). Our method reformulates the SOCCP as an equivalent nonlinear system of equations by introducing a regularized Chen-Harker-Kanzow-Smale smoothing function. At each iteration, Newton’s method is adopted to solve the system of equations approximately, which saves computation work compa...

Constraint reduction is an essential method because the computational cost of the interior point methods can be effectively saved. Park and O’Leary proposed a constraint-reduced predictor-corrector algorithm for semidefinite programming with polynomial global convergence, but they did not show its superlinear convergence. We first develop a constraintreduced algorithm for semidefinite programmi...

The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. In this paper, we propose and analyze the global and local convergence results of a novel Levenberg-Marquadt method for solving general nonlinear least squares problems. The proposed algorithm enjoys strong convergence properties (global convergence as well as quadratic ...

In order to enhance inter-particle cooperation and information sharing capabilities, an improved particle swarm algorithm optimization model is proposed by introducing the centroid of particle swarm in the standard PSO model to improve global optimum efficiency and accuracy of algorithm, then parameter selection guidelines are derived in the convergence of new algorithm. The results of Benchmar...

Gravitational search algorithm (GSA) has been successfully applied to many scientific and engineering applications in the past few years. In the original GSA and most of its variants, every agent learns from all the agents stored in the same elite group, namely K best . This type of learning strategy is in nature a fully-informed learning strategy, in which every agent has exactly the same glob...