 The adaptive coordinate descent method reaches the target value after only 325 function evaluations (about 70 times faster than coordinate descent), that is comparable to gradient-based methods.The algorithm has linear time complexity if update coordinate system every D iterations, it is also suitable for large-scale (D»100) non-linear optimization.First approaches to optimization using adaptive coordinate system were proposed already in the 1960s (see, e.g., Rosenbrock's method).