We compare algorithms for fundamental matrix computation, which we classify into “a posteriori correction”, “internal access”, and “external access”. Doing experimental comparison, we show that the 7-parameter Levenberg-Marquardt (LM) search and the extended FNS (EFNS) exhibit the best performance and that additional bundle adjustment does not increase the accuracy to any noticeable degree.

In this article a modified Levenberg-Marquardt method coupled with a Kaczmarz strategy for obtaining stable solutions of nonlinear systems of ill-posed operator equations is investigated. We show that the proposed method is a convergent regularization method. Numerical tests are presented for a non-linear inverse doping problem based on a bipolar model.

We examine the local convergence of the Levenberg–Marquardt method for the solution of nonlinear least squares problems that are rank-deficient and have nonzero residual. We show that replacing the Jacobian by a truncated singular value decomposition can be numerically unstable. We recommend instead the use of subset selection. We corroborate our recommendations by perturbation analyses and num...

An attempt has been made to fit the derived equation on the experimental inverse relaxation curves by employing Levenberg-Marquardt’s method for nonlinear regression and calculation of the constant values involved in the equation. The relaxation curves can be classified into ordinary relaxation, mixed relaxation and inverse relaxation curves. There seems to be good concurrence between the exper...