Unconstrained Optimization with Plotting
This example demonstrates several minimization examples from the family of unconstrained optimization problems. It finds the minimum of a simple quadratic function using BFGS, picks one minimum of a non-convex trigonometric function using BFGS, shows how to select individual gradient computation functions, compares the performance of BFGS with L-BFGS when finding the minimum of the Rosenbrock and Camel-3 functions and compares BFGS with the classical Newton method on the Rosenbrock function.
All examples are integrated into a small GUI application. User selects the example to run by clicking on the corresponding button. Each example creates individual output: the objective function is plotted and the solution or the path to the solution is shown. The comparison examples plot the objective function together with the path to the solution for each individual method. The progress of the optimization algorithm is plotted in a log-line plot above the function surface.
Last modified: March 30 2016 19:29