Extended Dai–Yuan Conjugate Gradient Strategy for Large-Scale Unconstrained Optimization with Applications to Compressive Sensing

Hamid Esmaeili, Majid Rostami, Morteza Kimiaei


We present a new spectral conjugate gradient method based on the Dai--Yuan strategy to solve large-scale unconstrained optimization problems with applications to compressive sensing. In our method, the numerator of conjugate gradient parameter is a convex combination from the maximum gradient norm value in some preceding iterates and the current gradient norm value. This combination will lead to produce the larger step-size far away from the optimizer and the smaller step-size close to it. In addition, the spectral parameter guarantees the descent property of the new generated direction in each iterate. The global convergence results are established under some standard assumptions. Numerical results are reported which indicate the promising behavior of the new procedure to solve large-scale unconstrained optimization and compressive sensing problems.

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