a hybrid cg algorithm for nonlinear unconstrained optimization with application in image restoration

This paper presents a new hybrid conjugate gradient method for solving  nonlinear unconstrained optimization problems; it is based on a combination of rmil  (rivaie-mustafa-ismail-leong)  and hsm  (hybrid sulaiman- mohammed) methods. the proposed algorithm enjoys the sufficient descent condition without depending on any line search; moreover, it is globally convergent under the usual and strong wolfe line search assumptions.  the performance of the algorithm is demonstrated through numerical experiments on a set of 100 test functions from [1] and four image restoration problems with two noise levels. the numerical comparisons with four existing methods show that the proposed method is promising and effective.