At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velo...

The convergence properties of the new Regularized Euclidean Residual method for solving general nonlinear least-squares and nonlinear equations problems are investigated. This method, derived from a proposal by Nesterov (2007), uses a model of the objective function consisting of the unsquared Euclidean linearized residual regularized by a quadratic term. At variance with previous analysis, its...

this paper deals with a globally convergent adaptive and sliding mode control of a cart-pole inverted pendulum for trajectory tracking in the presence of a bounded measurement noise and parameter uncertainty. two kinds of controllers have been used for evaluation of tracking error in presence of a bounded noise; as a result, we want to compare that at what time we can see the convergence of tra...

We study the long run behaviour of interactive Markov chains on in1nite product spaces. In view of microstructure models of 1nancial markets, the interaction has both a local and a global component. The convergence of such Markov chains is analyzed on the microscopic level and on the macroscopic level of empirical 1elds. We give su5cient conditions for convergence on the macroscopic level. Usin...

Regularized and stabilized sequential quadratic programming (SQP) methods are two classes of methods designed to resolve the numerical and theoretical difficulties associated with ill-posed or degenerate nonlinear optimization problems. Recently, a regularized SQP method has been proposed that allows convergence to points satisfying certain second-order KKT conditions (SIAM J. Optim., 23(4):198...

In [14], researchers studied the convergence of a dual gradient descent algorithm for sparse solutions of undetermined linear systems and showed that it has a globally linear convergence. In this paper we present another analysis. Mainly, we remove one assumption of completely full rankness on the linear system from the convergence result in [14] and provide with a different argument which sign...

Recently, we have proposed an iterative projection and contraction (PC) method for a class of linear complementarity problems (LCP). The method was showed to be globally convergent, but no statement could be made about the rate of convergence. In this paper, we develop a modified globally linearly convergent PC method for linear complementarity problems. Both the method and the convergence proo...

We propose a new method for mathematical programs with complementarity constraints that is globally convergent to B-stationary points. The method solves a linear program with complementarity constraints to obtain an estimate of the active set. It then fixes the activities and solves an equality-constrained quadratic program to obtain fast convergence. The method uses a filter to promote global ...