has exactly one minimum for (at ) and exactly two minima for —the global minimum at and a local minimum near . This result is obtained by setting the gradient of the function equal to zero, noticing that the resulting equation is a rational function of . For small the polynomials can be determined exactly and Sturm's theorem can be used to determine the number of real roots, while the roots can be bounded in the region of . For larger this method breaks down due to the size of the coefficients involved.
Many of the stationary points of the function exhibit a regular pattern when plotted. This structure can be exploited to locate them.
The Rosenbrock function can be efficiently optimized by adapting appropriate coordinate system without using any gradient information and without building local approximation models (in contrast to many derivate-free optimizers). The following figure illustrates an example of 2-dimensional Rosenbrock function optimization by
adaptive coordinate descent from starting point . The solution with the function value can be found after 325 function evaluations.
Using the Nelder–Mead method from starting point with a regular initial simplex a minimum is found with function value after 185 function evaluations. The figure below visualizes the evolution of the algorithm.