Example for OLS with Levenberg-Marquardt Optimizer.
Lecture 17-18: Least Squares Optimization
Optimization for Least Square Problems
Gauss-Newton algorithm for solving non linear least squares explained
Lecture 13: Non-linear least squares and the Gauss-Newton method
Least squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. The nonlinear problem is usually solved by iterative refinement; at each iteration the system is approximated by a linear one, and thus the core calculation is similar in both cases.
- The first one is based on the Gauss-Newton method.
- The second one is the Levenberg-Marquardt method. Gauss-Newton / Levenberg-Marquardt Optimization
- .lazyEvaluation(false) -> what is this
- .maxEvaluations(1000) -> what is this
- .maxIterations(1000) -> what is this