this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix approximates the diagonal terms of the Hessian and can be computed at individual links using the same information required by the unscaled algorithm. We prove the convergence of the scaled algorithm and present simulation results that illustrate its superiority to the unscaled algorithm
We present a new gradient method that uses scaling and extra updating within the diagonal updating f...
This paper deals with the preconditioning of truncated Newton methods for the solution of large scal...
We present a new gradient method that uses scaling and extra updating within the diagonal updating f...
We proposed earlier an optimization approach to reactive flow control where the objective of the con...
In this paper, we propose an interior-point method for linearly constrained and possibly nonconvex-o...
In this thesis, we are mainly concerned with finding the numerical solution of nonlinear unconstrain...
We investigate projected scaled gradient (PSG) methods for convex minimization problems. These metho...
We evaluate the performance of different optimization techniques developed in the context of optical...
There are several benefits of taking the Hessian of the objective function into account when designi...
AbstractQuasi-Newton methods for unconstrained minimization generate a sequence of matrices that can...
This paper develops a modified quasi-Newton method for structured unconstrained optimization with pa...
A grid movement algorithm based on the linear elasticity method with multiple increments is presente...
© 2015 Elsevier Ltd. We analyze the performance of different limited-memory quasi-Newton methods for...
A grid movement algorithm based on the linear elasticity method with multiple increments is presente...
We propose a multi-time scale quasi-Newton based smoothed functional (QN-SF) algorithm for stochasti...
We present a new gradient method that uses scaling and extra updating within the diagonal updating f...
This paper deals with the preconditioning of truncated Newton methods for the solution of large scal...
We present a new gradient method that uses scaling and extra updating within the diagonal updating f...
We proposed earlier an optimization approach to reactive flow control where the objective of the con...
In this paper, we propose an interior-point method for linearly constrained and possibly nonconvex-o...
In this thesis, we are mainly concerned with finding the numerical solution of nonlinear unconstrain...
We investigate projected scaled gradient (PSG) methods for convex minimization problems. These metho...
We evaluate the performance of different optimization techniques developed in the context of optical...
There are several benefits of taking the Hessian of the objective function into account when designi...
AbstractQuasi-Newton methods for unconstrained minimization generate a sequence of matrices that can...
This paper develops a modified quasi-Newton method for structured unconstrained optimization with pa...
A grid movement algorithm based on the linear elasticity method with multiple increments is presente...
© 2015 Elsevier Ltd. We analyze the performance of different limited-memory quasi-Newton methods for...
A grid movement algorithm based on the linear elasticity method with multiple increments is presente...
We propose a multi-time scale quasi-Newton based smoothed functional (QN-SF) algorithm for stochasti...
We present a new gradient method that uses scaling and extra updating within the diagonal updating f...
This paper deals with the preconditioning of truncated Newton methods for the solution of large scal...
We present a new gradient method that uses scaling and extra updating within the diagonal updating f...