Details

Autor(en) / Beteiligte

• Yao, Shengwei
• Lu, Xiwen
• Wei, Zengxin
• Soares, Delfim

Titel

A Conjugate Gradient Method with Global Convergence for Large-Scale Unconstrained Optimization Problems

Ist Teil von

• Journal of Applied Mathematics, 2013-01, Vol.2013, p.1-9

Ort / Verlag

Hindawi Limiteds

Erscheinungsjahr

2013

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. This paper proposes a conjugate gradient method which is similar to Dai-Liao conjugate gradient method (Dai and Liao, 2001) but has stronger convergence properties. The given method possesses the sufficient descent condition, and is globally convergent under strong Wolfe-Powell (SWP) line search for general function. Our numerical results show that the proposed method is very efficient for the test problems.