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.

翻译：我们提出了一个基于高效量化高压列车代表制和通用最大矩阵数量原则的优化新程序。 我们展示了新的Tensor火车优化器(TTOPT)方法对各种任务的适用性,从最大限度地减少多层面功能到强化学习。 我们的算法优于流行的以进化为基础的方法,并因功能评价或执行时间(通常为相当的幅度)而优于这些方法。