This article proposes a structured diagonal Hessian approximation for solving non-linear
least-squares (NLS) problems. We devised a modified structured matrix that satisfies the …

In this paper, we present a brief survey of methods for solving nonlinear least-squares
problems. We pay specific attention to methods that take into account the special structure of …

Many problems in science and engineering can be formulated as nonlinear least-squares
(NLS) problems. Thus, the need for efficient algorithms to solve these problems can not be …

This research article develops two adaptive, efficient, structured non-linear least-squares
algorithms, NLS. The approach taken to formulate these algorithms is motivated by the …

The study of efficient iterative algorithms for addressing nonlinear least-squares (NLS)
problems is of great importance. The NLS problems, which belong to a special class of …

This work proposes a Jacobian-free strategy for addressing large-scale nonlinear least-
squares problems, in which structured secant conditions are used to define a diagonal …

In this paper, we present two choices of structured spectral gradient methods for solving
nonlinear least squares problems. In the proposed methods, the scalar multiple of identity …

Recently, structured nonlinear least-squares (NLS) based algorithms gained considerable
emphasis from researchers; this attention may result from increasingly applicable areas of …

This work proposes a structured diagonal Gauss–Newton algorithm for solving zero residue
nonlinear least-squares problems. The matrix corresponding to the Gauss–Newton direction …

Numerically the reconstructability of unknown parameters in inverse problems heavily relies
on the chosen data. Therefore, it is crucial to design an experiment that yields data that is …