  We present a novel procedure for optimization based on the combination of
efficient quantized tensor train representation and a generalized maximum
matrix volume principle. We demonstrate the applicability of the new Tensor
Train Optimizer (TTOpt) method for various tasks, ranging from minimization of
multidimensional functions to reinforcement learning. Our algorithm compares
favorably to popular evolutionary-based methods and outperforms them by the
number of function evaluations or execution time, often by a significant
margin.
