Algorithm 851: CG_DESCENT, a conjugate gradient method with guaranteed descent
By William W. Hager, Hongchao Zhang. TOMS, 2006
439 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Numerical Analysis
View Paper: https://www.semanticscholar.org/paper/bca1372d2f8c0e1a26139d8bdfc23f188f79a494
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Saman Babaie-Kafaki
- Reza Ghanbari
- William W. Hager
- Hongchao Zhang
- Yuhong Dai
- Klaus Schittkowski
- John C. Nash
- R. Varadhan
In Numerical Analysis
- Saman Babaie-Kafaki
- Reza Ghanbari
- William W. Hager
- Hongchao Zhang
- Yuhong Dai
- Klaus Schittkowski
- John C. Nash
- R. Varadhan
Citations in the corpus
- Unifying Optimization Algorithms to Aid Software System Users: optimx for R (2011, background): “McCullough and Renfro (2000) and (McCullough 2004, Chapter 8) point out that there are many reasons that a solver, unknowingly misused by a researcher, can return an incorrect answer.”
- The cyclic Barzilai-–Borwein method for unconstrained optimization (2006, methodology): “The parameters used by CG DESCENT are the default parameter values given in Hager & Zhang (2006) for version 1.1 of the code.”
- Two optimal Dai–Liao conjugate gradient methods (2015, methodology): “1),[12] and the CG_Descent algorithm [7] which can be considered as an adaptive DL method with the choice (1.”