Hanger-Zhang Conjugate Gradient Algorithm for Relaxation Conditions
Conjugate gradient(CG)algorithm is a classical algorithm for solving unconstrained quadratic op-timization problems,but it can not solve non-quadratic problems.To solve this problem,a new parameter is in-troduced based on Hager-Zhang conjugate gradient descent algorithm,and a relaxed CG descent algorithm is de-signed.The algorithm does not store Jacobian matrix in each iteration,so it can solve large-scale non-smooth problems.The results show that the algorithm not only meets the global convergence and has excellent numerical performance,but also can solve the monotone constraint equation.Therefore,it is more adaptable than other CG algorithms.
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