Derivative free optimization
Often when I tell people what I've been doing at work or for my thesis, they might not understand what is optimization and what do optimization methods do. I created this demo to show how certain derivative-free optimization methods work. Try modifying the parameters to see their effect, or hover/click them for an explanation. You can also try different methods on different functions, to see their behavior and if certain methods work better than others in practice. The chart displays a contour map of the optimization landscape. Within the circles the current solution decreases, and the goal of this optimization problem is to find the minimum.
inertia
0.60
cognitive
0.50
social
0.50
convergence progress0%
about the method
The Nelder-Mead method is a simplex-based direct search method for nonlinear optimization. It is a numerical technique that does not require the computation of derivatives, making it suitable for optimizing functions where the gradients are not available or computationally expensive to calculate. In comparison to DE and PSO, it is not a global optimization method and it might get stuck to a local minimum.