Results 81 to 90 of about 65,642 (305)

An Improved Mixed Conjugate Gradient Method

, 2012
Conjugate gradient method is an important and efficient method to solve the unconstrained optimization problems, especially for large scale problems.
Zong, Jizhou, Wang, Xiaodong, Jia, Wen
core   +1 more source

Development of a Spectral Conjugate Gradient Method for Solving Optimization Problems

Al-Rafidain Journal of Computer Sciences and Mathematics
Conjugate gradient methods have been favored to use for their efficiency in solving large-scale unconstrained optimization problems, primarily because of their low memory requirements and exclusive to use the first-order derivative information.
Kamal Jameel Murad, Salah Gazi Shareef
doaj   +1 more source

EDNRB‐dependent endothelin signaling reduces proliferation and promotes proneural‐to‐mesenchymal transition in gliomas

Molecular Oncology, EarlyView.
Glioma cells mainly express the endothelin receptor EDNRB, while EDNRA is restricted to a perivascular tumor subpopulation. Endothelin signaling reduces glioma cell proliferation while promoting migration and a proneural‐to‐mesenchymal transition associated with poor prognosis. This pathway activates Ca2+, K+, ERK, and STAT3 signalings and is regulated
Donovan Pineau, Leonor Garcia, Hugo Arnold, Antonija Hanžek, Maialen Arrieta, Laurent R Gauthier, Christine Granotier‐Beckers, François D Boussin, Amaury Herbet, Marie Hautière, Valentin Asei‐Ceschino, Clémentin Jacques, Laura Brard, Thomas Harnois, Valérie Coronas, Bruno Constantin, Aurélien Chatelier, Jean Chemin, Serge Urbach, Martial Seveno, Szimonetta Hideg, Chantal Ripoll, Kasandra Aguilar‐Cázarez, Min Zheng, Guo‐Hao Huang, Sheng‐Qing Lv, Lei Zhang, Philippe Rondard, Laurent Prezeau, Jean‐Philippe Pin, Hugues Duffau, Luc Bauchet, Valérie Rigau, Franck Denat, Charles Truillet, Didier Boquet, Jean‐Philippe Hugnot   +36 more
wiley   +1 more source

New iterative conjugate gradient method for nonlinear unconstrained optimization

, 2022
Conjugate gradient methods (CG) are an important class of methods for solving unconstrained optimization problems, especially for large-scale problems. Recently, they have been much studied.
Badreddine Sellami, Sabrina Ben Hanachi, Mohammed Belloufi   +2 more
core   +1 more source

Global Convergence of a New Coefficient Nonlinear Conjugate Gradient Method

, 2018
Venue: Avillion Legacy MelakaNonlinear conjugate gradient (CG) methods are widely used in optimization field due to its efficiency for solving a large scale unconstrained optimization problems.
Shazlyn Milleana Shaharuddin., Mohd Rivaie, Mustafa Mamat, Nur Syarafina Mohamed, (UniKL MITEC)   +4 more
core  

Conjugate Gradient Method for Solving System of Nonlinear Equations

, 2005
In this thesis, the conjugate gradient method will be extended to solve symmetric and non-symmetric nonlinear equations. Several variations of the conjugate gradient method are introduced and investigated.
Honrao, Abhijit Prakash
core   +1 more source

Globally convergence of nonlinear conjugate gradient method for unconstrained optimization [PDF]

, 2017
The conjugate gradient method is a useful and powerful approach for solving large-scale minimization problems. In this paper, a new nonlinear conjugate gradient method is proposed for large-scale unconstrained optimization.
B. Sellami, M. Belloufi, Belloufi, M., Chaib, Y., Y. Chaib, Sellami, B.   +5 more
core   +1 more source

Estimating the unemployment rate using least square and conjugate gradient methods

, 2018
Unemployment rate is one of the major issues among Malaysian citizens. The unemployment rate indicates the percentage of the total workforce who are actively seeking employment and currently unemployed.
Nur Hamizah Abdul Ghani, Norhaslinda Zull, Mohd Rivaie, Syazni Syoid, Mustafa Mamat, Nur Syarafina Mohamed, (UniKL MITEC)   +6 more
core  

Conjugate gradient method for the linear complementarity problem with S-matrix

, 2008
In this paper, we present a conjugate gradient method for solving the linear complementarity problem that involves an S-matrix. At each step, we solve a lower-dimensional system of linear equations by conjugate gradient method.
Li DH(李董辉), Nie YY(聂义勇), Zeng JP(曾经平), Qing-Na Li   +3 more
core  

Gradient pursuits

, 2008
Sparse signal approximations have become a fundamental tool in signal processing with wide ranging applications from source separation to signal acquisition.
Blumensath, T., Davies, Michael; id_orcid, Davies, M.E.   +2 more
core   +1 more source