Nonlinear least squares (NLS) problems arise in many applications. The common solvers require to compute and store the corresponding Jacobian matrix explicitly, which is too expensive for large problems. In this paper, we propose an effective Jacobian free method especially for large NLS problems because of the novel combination of using automatic differentiation for J(x)v and J (x)v along with...

The velocity-vorticity-pressure formulation of the steady-state incompressible Navier-Stokes equations in two dimensions is cast as a nonlinear least squares problem in which the functional is a weighted sum of squared residuals. A finite element discretization of the functional is minimized by a trust-region method in which the trust-region radius is defined by a Sobolev norm and the trust-reg...

In this paper we consider several algorithms for reducing the storage when using a quasi-Newton method in a dogleg–trust region setting for minimizing functions of many variables. Secant methods require O(n2) locations to store an approximate Hessian and O(n2) operations per iteration when minimizing a function of n variables. This storage requirement becomes impractical when n becomes large. O...

We focus on the numerical solution of medium scale bound-constrained systems of nonlinear equations. In this context, we consider an affinescaling trust region approach that allows a great flexibility in choosing the scaling matrix used to handle the bounds. The method is based on a dogleg procedure tailored for constrained problems and so, it is named Constrained Dogleg method. It generates on...

We focus on the numerical solution of medium scale bound-constrained systems of nonlinear equations. In this context, we consider an affinescaling trust region approach that allows a great flexibility in choosing the scaling matrix used to handle the bounds. The method is based on a dogleg procedure tailored for constrained problems and so, it is named Constrained Dogleg method. It generates on...

The dogleg method is a classical trust-region technique for globalizing Newton’s method. While it is widely used in optimization, including large-scale optimization via truncatedNewton approaches, its implementation in general inexact Newton methods for systems of nonlinear equations can be problematic. In this paper, we first outline a very general dogleg method suitable for the general inexac...

This paper describes a method of dogleg trust-region steps, or restricted Levenberg-Marquardt steps, based on a projection process onto the Krylov subspaces for neural networks nonlinear least squares problems. In particular, the linear conjugate gradient (CG) method works as the inner iterative algorithm for solving the linearized Gauss-Newton normal equation, whereas the outer nonlinear algor...

We focus on the numerical solution of medium scale bound-constrained systems of nonlinear equations. In this context, we consider an affinescaling trust region approach that allows a great flexibility in choosing the scaling matrix used to handle the bounds. The method is based on a dogleg procedure tailored for constrained problems and so, it is named Constrained Dogleg method. It generates on...

Within the framework of affine scaling trust-region methods for bound constrained problems, we discuss the use of a inexact dogleg method as a tool for simultaneously handling the trust-region and the bound constraints while seeking for an approximate minimizer of the model. Focusing on bound-constrained systems of nonlinear equations, an inexact affine scaling method for large scale problems, ...

In this paper, the particular solution technique for inverse simulation applied to the quadrotor maneuvering flight is investigated. The ‎trust-region dogleg (DL) technique which is proposed alleviates the weakness of Newton’s method used for numerical differentiation of system states in the solution process. The proposed technique emphasizes global convergence solution to the inverse simulatio...