On Spectral Dai-Yuan Structured Technique for Solving Nonlinear Least-Squares Optimisation

Abstract

Handling nonlinear least squares (NLS) problems as unconstrained optimisation problems has led to the development and adaptation of numerous numerical approaches. Calculating the Hessian matrix expensively for each iteration is one of the problems with the current numerical approaches for solving NLS problems. Some methods designed to rely solely on first-derivative information while ignoring second-order details tend to underperform when dealing with problems involving non-zero residuals. This article suggests a modification to the Dai-Yuan type (DY) formula by deriving a spectral parameter for the proposed search direction. The sufficient descent results of the new formula are established using the standard assumptions under the strong Wolfe line search. Furthermore, experiments are conducted on some benchmark functions to demonstrate the computational efficiency of the proposed technique. The results highlight the effectiveness of the algorithm compared to existing methods.