A New Hybrid Conjugates Gradient Algorithm for Unconstraint Optimization Problems
In this paper, we present a new hybrid conjugate gradient strategy that is both efficient and effective for solving unconstrained optimization problems. The parameter is derived from a convex combination of the and the conjugate gradient methods. We demonstrated that this strategy is globally convergent under strong Wolfe line search conditions, and that the recommended hybrid CG method is capable of creating a descending search direction at each iteration. Numerical results are presented in this study, demonstrating that the proposed technique is both efficient and promising.
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