A family of spectral conjugate gradient methods with strong convergence and its applications in image restoration and machine learning

Abstract

In this paper, we propose a family of spectral conjugate gradient methods for solving unconstrained optimization problems. Specifically, we provide two classes of bounded spectral parameters to be chosen, design a new truncation scheme of the non-negative conjugate parameter and set a restart procedure in our search direction. Independently of the specific spectral parameter, conjugate parameter and line search criterion, we prove that our proposed family satisfies the sufficient descent condition. We also prove its strong convergence under mild assumptions and the weak Wolfe line search. Numerical comparisons with other methods demonstrate the outstanding performances of our algorithm for solving medium–large-scale unconstrained optimization, image restoration and machine learning problems.