The paper contains a description of an efficient algorithm for linearly constrained nonlinear minimax approximation. This algorithm uses an elimination of constraints together with the product-form variable metric updates. The main advantage of the new algorithm consists in a simple determination of the direction vector. No quadratic programming subproblem has to be solved. The efficiency of th...

This paper considers the fixed point problem for a nonexpansive mapping on a real Hilbert space and proposes novel line search fixed point algorithms to accelerate the search. The termination conditions for the line search are based on the well-known Wolfe conditions that are used to ensure the convergence and stability of unconstrained optimization algorithms. The directions to search for fixe...

Autonomous surface vehicles are with increasing popularity being seen in various applications where automatic control plays an important role. In this paper the problem of twodimensional trajectory tracking for autonomous marine surface vehicles is addressed using Model Predictive Control (MPC). At each time step, the reference trajectories of a vessel are assumed to be known over a finite time...

This paper describes an active-set algorithm for nonlinear programming that solves a parametric linear programming subproblem at each iteration to generate an estimate of the active set. A step is then computed by solving an equality constrained quadratic program based on this active-set estimate. This approach respresents an extension of the standard sequential linear-quadratic programming (SL...

This paper develops an event-triggered optimal control method that can deal with asymmetric input constraints for nonlinear discrete-time systems. The implementation is based on explainable global dual heuristic programming (XGDHP) technique. Different from traditional GDHP, the required derivatives of cost function in proposed are computed by explicit analytical calculations, which makes XGDHP...

Asymptotic stationarity and regularity conditions turned out to be quite useful study the qualitative properties of numerical solution methods for standard nonlinear complementarity constrained programs. In this paper, we first extend these notions optimization problems with nonsmooth but Lipschitzian data functions in order find reasonable asymptotic terms Clarke's Mordukhovich's subdifferenti...

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 report on the theory and implementation of a global optimization solver for general constrained nonlinear programming problems based on Variable Neighbourhood Search, and we give comparative computational results on several instances of continuous nonconvex problems. Compared to an efficient multi-start global optimization solver, the VNS solver proposed appears to be significantly faster.