Litcius/Paper detail

Modified Hager–Zhang conjugate gradient methods via singular value analysis for solving monotone nonlinear equations with convex constraint

Jamilu Sabi’u, Abdullah Shah, Mohammed Yusuf Waziri, Kabiru Ahmed

2020International Journal of Computational Methods41 citationsDOI

Abstract

Following a recent attempt by Waziri et al. [2019] to find an appropriate choice for the nonnegative parameter of the Hager–Zhang conjugate gradient method, we have proposed two adaptive options for the Hager–Zhang nonnegative parameter by analyzing the search direction matrix. We also used the proposed parameters with the projection technique to solve convex constraint monotone equations. Furthermore, the global convergence of the methods is proved using some proper assumptions. Finally, the efficacy of the proposed methods is demonstrated using a number of numerical examples.

Topics & Concepts

MathematicsConjugate gradient methodMonotone polygonApplied mathematicsGradient methodConstraint (computer-aided design)Nonlinear conjugate gradient methodNonlinear systemDerivation of the conjugate gradient methodProjection (relational algebra)Regular polygonConvergence (economics)Monotonic functionMathematical optimizationProjection methodConvex optimizationMathematical analysisGradient descentDykstra's projection algorithmAlgorithmComputer scienceGeometryMachine learningEconomicsPhysicsQuantum mechanicsEconomic growthArtificial neural networkAdvanced Optimization Algorithms ResearchIterative Methods for Nonlinear EquationsFractional Differential Equations Solutions