A Family of Inertial Three‐Term CGPMs for Large‐Scale Nonlinear Pseudo‐Monotone Equations With Convex Constraints
Jinbao Jian, Qiongxuan Huang, Jianghua Yin, Guodong Ma
Abstract
ABSTRACT This article presents and analyzes a family of three‐term conjugate gradient projection methods with the inertial technique for solving large‐scale nonlinear pseudo‐monotone equations with convex constraints. The generated search direction exhibits good properties independent of line searches. The global convergence of the family is proved without the Lipschitz continuity of the underlying mapping. Furthermore, under the locally Lipschitz continuity assumption, we conduct a thorough analysis related to the asymptotic and non‐asymptotic global convergence rates in terms of iteration complexity. To our knowledge, this is the first iteration‐complexity analysis for inertial gradient‐type projection methods, in the literature, under such a assumption. Numerical experiments demonstrate the computational efficiency of the family, showing its superiority over three existing inertial methods. Finally, we apply the proposed family to solve practical problems such as ‐regularized logistic regression, sparse signal restoration and image restoration problems, highlighting its effectiveness and potential for real‐world applications.