Introducing Moment: A toolkit for semi-definite programming with moment matrices
<|reference_start|>Introducing Moment: A toolkit for semi-definite programming with moment matrices: Non-commutative polynomial optimization is a powerful technique with numerous applications in quantum nonlocality, quantum key distribution, causal inference, many-body physics, amongst others. The standard approach is to reduce such optimizations to a hierarchy of semi-definite programs, which can be solved numerically using well-understood interior-point methods. A key, but computationally costly, step is the formulation of moment matrices, whose size (and hence cost) grows exponentially with the depth of the hierarchy. It is therefore essential to have highly-optimized software to construct moment matrices. Here, we introduce Moment: a toolkit that produces moment matrix relaxations from the specification of a non-commutative optimization problem. In order to obtain the absolute best performance, Moment is written in C++, and for convenience of use provides an interface via MATLAB. We benchmark Moment's performance, and see that it can be up to four orders of magnitude faster than current software with similar functionality.<|reference_end|>
@article{garner2024introducing,
    title={Introducing Moment: A toolkit for semi-definite programming with moment
  matrices},
    author={Andrew J. P. Garner and Mateus Ara'ujo},
    journal={arXiv preprint arXiv:2406.15559},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15559},
    primaryClass={quant-ph cs.MS math.OC}
}