We consider the inexact restoration and the composite-step sequential quadratic programming (SQP) methods, and relate them to the so-called perturbed SQP framework. In particular, iterations of the methods in question are interpreted as certain structured perturbations of the basic SQP iterations. This gives a different insight into local behaviour of those algorithms, as well as improved or di...

The sequential quadratic programming (SQP) algorithm has been one of the most successful general methods for solving nonlinear constrained optimization problems. We provide an introduction to the general method and show its relationship to recent developments in interior-point approaches. We emphasize large-scale aspects.

In this paper we address the problem of finding a geodesic curve that best fits a given set of time-labeled points on a sphere. Since the corresponding normal equations are highly non-linear, we formulate the problem as a constrained nonlinear optimization problem and solve it using the routine fmincon from MATLAB with the SQP (Sequential Quadratic Programming) algorithm.

We consider the use of the sequential quadratic programming (SQP) technique for solving the inequality constrained minimization problem min x f(x) subject to: g i (x) 0; i = 1; : : :; m: SQP methods require the use of an auxiliary function, called a merit function or line-search function, for assessing the steps that are generated. We derive a merit function by adding slack variables to create ...

A method for computing minimum cost trajectory planning for industrial robot manipulators is presented. The objective function minimizes the cost function with the constraints being joint positions, velocities, jerks and torques by considering dynamic equations of motion. A clamped cubic spline is used to represent the trajectory. This is a non-linear constrained optimization problem. The probl...

0020-0255/$ see front matter 2012 Elsevier Inc http://dx.doi.org/10.1016/j.ins.2012.06.003 ⇑ Corresponding author. E-mail addresses: [email protected] (W.X. Xu) (X.B. Gu). This paper presents a novel robust hybrid particle swarm optimization (RHPSO) based on piecewise linear chaotic map (PWLCM) and sequential quadratic programming (SQP). The aim of the present research is to develop a new singl...

The reduced Hessian SQP algorithm presented in is developed in this paper into a practical method for large scale optimization The novelty of the algorithm lies in the incorporation of a correction vector that approximates the cross term ZWY pY This improves the stability and robustness of the algorithm without increasing its computational cost The paper studies how to implement the algorithm e...

We propose an SQP-type algorithm for solving nonlinear second-order cone programming (NSOCP) problems. At every iteration, the algorithm solves a convex SOCP subproblem in which the constraints involve linear approximations of the constraint functions in the original problem and the objective function is a convex quadratic function. Those subproblems can be transformed into linear SOCP problems...

In this work we discuss global convergence of a general filter algorithm that does not depend neither on the definition of the forbidden region, which can be given by the original or slanting filter rule, nor on the way in which the step is computed. This algorithm basically consists of calculating a point not forbidden by the filter from the current point. Assuming that this step must be effic...