Results 261 to 270 of about 36,010,976 (311)

Preconditioned Spectral Gradient Method

Numerical Algorithms, 2002
Modifications of the spectral gradient method are presented, which globalize the method and present strategies to apply preconditioning techniques. The condition of uniform positive definiteness of the preconditioning matrices is replaced with mild conditions on the search directions.
Francisco Luengo, Marcos Raydan, William Kile Glunt, Tom L. Hayden   +3 more
openaire   +2 more sources

A cyclic projected gradient method

Computational Optimization and Applications, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Simon Setzer, Gabriele Steidl, Jan Morgenthaler   +2 more
openaire   +2 more sources

Deconvolution by the conjugate gradient method

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
Since it is practically difficult to generate and propagate an impulse, often a system is excited by a narrow time domain pulse. The output is recorded and then a numerical deconvolution is often done to extract the impulse response of the object.
Tapan K. Sarkar, Fung I. Tseng, Soheil A. Dianat, Bruce Z. Hollmann   +3 more
openaire   +1 more source

A New Conjugate Gradient Method for Moving Force Identification of Vehicle–Bridge System

Journal of Vibration Engineering & Technologies, 2022
Chengsheng Luo, Linjun Wang, Youxiang Xie, Baojia Chen   +3 more
semanticscholar   +1 more source

Conditional gradient method for multiobjective optimization

Computational optimization and applications, 2021
P. Assunção, O. P. Ferreira, L. F. Prudente   +2 more
semanticscholar   +1 more source

New stepsizes for the gradient method

Optimization Letters, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cong Sun 0002, Jin-Peng Liu
openaire   +2 more sources

The Gradient Method

1970
For the sake of simplicity and clarity we shall first consider the problem where there are no constraints of the form (2) or (3). We shall also assume that the final time T is given.
openaire   +1 more source

Gradient Method with Retards and Generalizations

SIAM Journal on Numerical Analysis, 1998
Let \({\mathbf A}\) be a real symmetric positive definite \(n \times n\) matrix and \(b\) be a real \(n\)-dimensional vector. The unique solution of the equation \({\mathbf A}x= b\) is the argument of the global minimum of the function \(f(x)\equiv \frac{1}{2} x^{T} {\mathbf A}x - b^{T}x\). Let us denote \(g(x)= {\mathbf A}x - b\) and let \(\lambda (x)\
Friedlander, A., Martínez, J. M., Molina, B., Raydan, M.   +3 more
openaire   +1 more source

Convergence of memory gradient methods

International Journal of Computer Mathematics, 2008
In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence.
Zhen-Jun Shi, Jinhua Guo 0001
openaire   +1 more source

Duality in conjugate gradient methods

Numerical Algorithms, 1999
The authors present the reverse algorithms of \textit{Cs. J. Hegedüs} [Comput. Math. Appl. 21, No. 1, 71-85 (1991; Zbl 0727.65023)] in a new perspective and show how they are related to the more conventional algorithms if the latter is regarded as solving problems involving the original preconditioning matrices.
Broyden C. G., Foschi P.
openaire   +3 more sources