ttopt 0.6.2

Multivariate function optimizer based on the tensor train approach.

ttopt

Description

Gradient-free optimization method for multivariable functions based on the low rank tensor train (TT) format and maximal-volume principle.

Please, see also our software product teneva which provides a very compact implementation of basic operations in the TT-format.

Installation

You can install the ttopt package for python >= 3.7 with pip:

pip install ttopt==0.6.2

Examples

The demo-scripts with detailed comments are collected in the folder demo:

• base.py - we find the minimum for the 10-dimensional function with vectorized input;
• qtt.py - we do almost the same as in the base.py script, but use the QTT-based approach (note that results are much more better then in the base.py example);
• qtt_max.py - we do almost the same as in the qtt.py, but consider the maximization task;
• qtt_100d.py - we do almost the same as in the qtt.py script, but approximate the 100-dimensional function;
• vect.py - we find the minimum for the simple analytic function with "simple input" (the function is not vectorized);
• cache.py - we find the minimum for the simple analytic function to demonstrate the usage of the cache;
• tensor.py - in this example we find the minimum for the multidimensional array/tensor (i.e., discrete function);
• tensor_init - we do almost the same as in the tensor.py script, but we use special method of initialization (instead of a random tensor, we select a set of starting multi-indices for the search).

Authors

• Andrei Chertkov
• Ivan Oseledets
• Roman Schutski
• Konstantin Sozykin

Citation

If you find this approach and/or code useful in your research, please consider citing:

@article{sozykin2022ttopt,
    author    = {Sozykin, Konstantin and Chertkov, Andrei and Schutski, Roman and Phan, Anh-Huy and Cichocki, Andrzej and Oseledets, Ivan},
    year      = {2022},
    title     = {{TTOpt}: {A} maximum volume quantized tensor train-based optimization and its application to reinforcement learning},
    journal   = {Advances in Neural Information Processing Systems},
    volume    = {35},
    pages     = {26052--26065},
    url       = {https://proceedings.neurips.cc/paper_files/paper/2022/hash/a730abbcd6cf4a371ca9545db5922442-Abstract-Conference.html}
}

Please, note that the calculations presented in this paper correspond to version <0.5.0 of the ttopt package (and to very old version of the teneva package), to run the calculations, please use the appropriate version. In the new versions >=0.6.0, we have removed all the corresponding folders in the folder computations_old. In the future, we will try to update the interface of these experiments.

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