An improved Dai-Liao-style hybrid conjugate gradient-based method for solving unconstrained nonconvex optimization and extension to constrained nonlinear monotone equations

In this work, for unconstrained optimization, we introduce an improved Dai-Liao-style hybrid conjugate gradient method based on the hybridization-based self-adaptive technique, and the search direction generated fulfills the sufficient descent and trust region properties regardless of any line search. The global convergence is established under standard Wolfe line search and common assumptions. Then, combining the hyperplane projection technique and a new self-adaptive line search, we extend the proposed conjugate gradient method and obtain an improved Dai-Liao-style hybrid conjugate gradient projection method to solve constrained nonlinear monotone equations. Under mild conditions, we obtain its global convergence without Lipschitz continuity. In addition, the convergence rates for the two proposed methods are analyzed, respectively. Finally, numerical experiments are conducted to demonstrate the effectiveness of the proposed methods.