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Research output: Contribution to Journal/Magazine › Conference article › peer-review
Research output: Contribution to Journal/Magazine › Conference article › peer-review
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TY - JOUR
T1 - Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes
AU - Chevallier, Augustin
AU - Cazals, Frédéric
AU - Fearnhead, Paul
PY - 2022/3/28
Y1 - 2022/3/28
N2 - Computing the volume of a polytope in high dimensions is computationally challenging but has wide applications. Current state-of-the-art algorithms to compute such volumes rely on efficient sampling of a Gaussian distribution restricted to the polytope, using e.g. Hamiltonian Monte Carlo. We present a new sampling strategy that uses a Piecewise Deterministic Markov Process. Like Hamiltonian Monte Carlo, this new method involves simulating trajectories of a non-reversible process and inherits similar good mixing properties. However, importantly, the process can be simulated more easily due to its piecewise linear trajectories - and this leads to a reduction of the computational cost by a factor of the dimension of the space. Our experiments indicate that our method is numerically robust and is one order of magnitude faster (or better) than existing methods using Hamiltonian Monte Carlo. On a single core processor, we report computational time of a few minutes up to dimension 500.
AB - Computing the volume of a polytope in high dimensions is computationally challenging but has wide applications. Current state-of-the-art algorithms to compute such volumes rely on efficient sampling of a Gaussian distribution restricted to the polytope, using e.g. Hamiltonian Monte Carlo. We present a new sampling strategy that uses a Piecewise Deterministic Markov Process. Like Hamiltonian Monte Carlo, this new method involves simulating trajectories of a non-reversible process and inherits similar good mixing properties. However, importantly, the process can be simulated more easily due to its piecewise linear trajectories - and this leads to a reduction of the computational cost by a factor of the dimension of the space. Our experiments indicate that our method is numerically robust and is one order of magnitude faster (or better) than existing methods using Hamiltonian Monte Carlo. On a single core processor, we report computational time of a few minutes up to dimension 500.
KW - stat.CO
KW - cs.LG
M3 - Conference article
VL - 151
SP - 10146
EP - 10160
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
SN - 1938-7228
ER -