شماره ركورد كنفرانس :
5432
عنوان مقاله :
A Hybrid Conjugate Gradient Method Based on an Extended Least-Squares Model
پديدآورندگان :
Toofan Mariya m_toofan@semnan.ac.ir Department of Mathematics, Semnan University Semnan, Iran , Babaie–Kafaki Saman saman.babaiekafaki@unibz.it Faculty of Engineering, Free University of Bozen–Bolzano Bolzano, Italy
كليدواژه :
Unconstrained optimization , conjugate gradient algorithm , least , squares model , ellipsoid norm , secant condition.
عنوان كنفرانس :
شانزدهمين كنفرانس بين المللي انجمن ايراني تحقيق در عمليات
چكيده فارسي :
Inspired by Andrei s approach of combining the conjugate gradient parameters convexly, a hybridization of the Hestenes–Stiefel and Dai–Yuan conjugate gradient methods is proposed. The hybridization parameter is determined by using the ellipsoid norm as an extension of the Euclidean norm, in a least-squares framework. Efficiency of the suggested hybrid conjugate gradient method in the sense of the Dolan–Moré performance profile is depicted as well.
