Hi Andrew,
The Levenberg-Marquardt algorithm and the Golden Section search method are two different optimization algorithms that are generally used for different types of problems.
The Levenberg-Marquardt algorithm is typically used for non-linear least squares problems, which are common in curve fitting and data fitting scenarios. The algorithm combines the gradient descent method and the Gauss-Newton method to minimize a sum of squared function values.
On the other hand, the Golden Section search method is a line search method used to find a local minimum of a unimodal function in one dimension without the use of derivatives.
Combining these two methods is not a standard approach because they are designed for different optimization tasks. The Levenberg-Marquardt algorithm already includes a mechanism for adjusting the step size, which is somewhat similar to the goal of the Golden Section method in finding an optimal step size in one-dimensional problems.
In MATLAB, if you want to use the Levenberg-Marquardt algorithm, you can use the “lsqcurvefit” or “lsqnonlin” functions from the Optimization Toolbox, which have the option to specify the Levenberg-Marquardt algorithm as the optimization method.
Please refer to the MathWorks documentation for “lsqcurvefit” function –
Please refer to the MathWorks documentation for “lsqnonlin” function –
Hope this helps.
