Line Searches

2022 ◽  
pp. 21-43
Author(s):  
J J McKeown ◽  
D Meegan ◽  
D Sprevak
Keyword(s):  
Line Searches

scholarly journals On the Strong Convergence of a Sufficient Descent Polak-Ribière-Polyak Conjugate Gradient Method

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Min Sun ◽  
Jing Liu
Keyword(s):  
Strong Convergence ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Large Scale ◽  
Optimization Problems ◽  
Line Searches ◽  
Unconstrained Optimization Problems ◽  
Sufficient Descent ◽  
Large Scale Unconstrained Optimization

Recently, Zhang et al. proposed a sufficient descent Polak-Ribière-Polyak (SDPRP) conjugate gradient method for large-scale unconstrained optimization problems and proved its global convergence in the sense thatlim infk→∞∥∇f(xk)∥=0when an Armijo-type line search is used. In this paper, motivated by the line searches proposed by Shi et al. and Zhang et al., we propose two new Armijo-type line searches and show that the SDPRP method has strong convergence in the sense thatlimk→∞∥∇f(xk)∥=0under the two new line searches. Numerical results are reported to show the efficiency of the SDPRP with the new Armijo-type line searches in practical computation.

Line-Searches

2003 ◽  
pp. 37-50
Author(s):  
J. Frédéric Bonnans ◽  
J. Charles Gilbert ◽  
Claude Lemaréchal ◽  
Claudia A. Sagastizábal
Keyword(s):  
Line Searches

scholarly journals Kband SINFONI spectra of twoz~ 5 submillimeter galaxy systems: upper limits to the unobscured star formation from [O II] optical emission line searches

2016 ◽  
Vol 594 ◽  
pp. A74 ◽  
Author(s):  
Guilherme S. Couto ◽  
Luis Colina ◽  
Javier Piqueras López ◽  
Thaisa Storchi-Bergmann ◽  
Santiago Arribas
Keyword(s):  
Star Formation ◽  
Emission Line ◽  
Optical Emission ◽  
Line Searches ◽  
Upper Limits

A penalty method for nonlinear programming

2019 ◽  
Vol 53 (1) ◽  
pp. 29-38
Author(s):  
Larbi Bachir Cherif ◽  
Bachir Merikhi
Keyword(s):  
Nonlinear Programming ◽  
Convex Programming ◽  
Penalty Method ◽  
Line Search ◽  
Penalty Methods ◽  
Descent Direction ◽  
Type Method ◽  
Numerical Tests ◽  
Line Searches ◽  
Majorant Function

This paper presents a variant of logarithmic penalty methods for nonlinear convex programming. If the descent direction is obtained through a classical Newton-type method, the line search is done on a majorant function. Numerical tests show the efficiency of this approach versus classical line searches.

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