A hybridization of the Hestenes-Stiefel and Dai-Yuan conjugate gradient methods based on a least-squares approach

Following Andrei's approach of combining the conjugate gradient parameters convexly, a hybridization of the Hestenes-Stiefel (HS) and Dai-Yuan conjugate gradient (CG) methods is proposed. The hybridization parameter is computed by solving the least-squares problem of minimizing the distance between search directions of the hybrid method and a three-term conjugate gradient method proposed by Zhang et al. which possesses the sufficient descent property. Also, Powell's non-negative restriction of the HS CG parameter is employed in the hybrid method. A brief global convergence analysis is made without convexity assumption on the objective function. Comparative testing results are reported; they demonstrate efficiency of the proposed hybrid CG method in the sense of the Dolan-More performance profile.