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We present a novel procedure for optimization based on the combination ofefficient quantized tensor train representation and a generalized maximummatrix volume principle. We demonstrate the applicability of the new TensorTrain Optimizer (TTOpt) method for various tasks, ranging from minimization ofmultidimensional functions to reinforcement learning. Our algorithm comparesfavorably to popular evolutionary-based methods and outperforms them by thenumber of function evaluations or execution time, often by a significantmargin.

TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning