
The qLevenbergMarquardt method for unconstrained nonlinear optimization
A qLevenbergMarquardt method is an iterative procedure that blends a q...
read it

On the choice of initial guesses for the NewtonRaphson algorithm
The initialization of equationbased differentialalgebraic system model...
read it

Robust and Efficient Optimization Using a MarquardtLevenberg Algorithm with R Package marqLevAlg
Optimization is an essential task in many computational problems. In sta...
read it

Iterative Reweighted Minimization Methods for l_p Regularized Unconstrained Nonlinear Programming
In this paper we study general l_p regularized unconstrained minimizatio...
read it

Weight Design of Distributed Approximate Newton Algorithms for Constrained Optimization
Motivated by economic dispatch and linearlyconstrained resource allocat...
read it

A new search direction for fullNewton step infeasible interiorpoint method in linear optimization
In this paper, we study an infeasible interiorpoint method for linear o...
read it

A Family of Iterative GaussNewton Shooting Methods for Nonlinear Optimal Control
This paper introduces a family of iterative algorithms for unconstrained...
read it
The qGaussNewton method for unconstrained nonlinear optimization
A qGaussNewton algorithm is an iterative procedure that solves nonlinear unconstrained optimization problems based on minimization of the sum squared errors of the objective function residuals. Main advantage of the algorithm is that it approximates matrix of qsecond order derivatives with the firstorder qJacobian matrix. For that reason, the algorithm is much faster than qsteepest descent algorithms. The convergence of qGN method is assured only when the initial guess is close enough to the solution. In this paper the influence of the parameter q to the nonlinear problem solving is presented through three examples. The results show that the qGD algorithm finds an optimal solution and speeds up the iterative procedure.
READ FULL TEXT
Comments
There are no comments yet.