9 citations · 10 across the 4 of their papers we have counts for
6 papers
Tensor cross interpolation for global discrete optimization with application to Bayesian network inference
Sergey Dolgov, Dmitry Savostyanov
Global discrete optimization is notoriously difficult due to the lack of gradient information and the curse of dimensionality, making exhaustive search infeasible. Tensor cross app…
Tensor product algorithms for inference of contact network from epidemiological data
Sergey Dolgov, Dmitry Savostyanov
We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black--box Bayesian optimisation framework this problem reduce…
Guessing Random Additive Noise Decoding of Network Coded Data Transmitted over Burst Error Channels
Ioannis Chatzigeorgiou, Dmitry Savostyanov
We consider a transmitter that encodes data packets using network coding and broadcasts coded packets. A receiver employing network decoding recovers the data packets if a sufficie…
Tensor product approach to modelling epidemics on networks
Sergey V. Dolgov, Dmitry V. Savostyanov
To improve mathematical models of epidemics it is essential to move beyond the traditional assumption of homogeneous well--mixed population and involve more precise information on…
Parallel cross interpolation for high-precision calculation of high-dimensional integrals
Sergey Dolgov, Dmitry Savostyanov
We propose a parallel version of the cross interpolation algorithm and apply it to calculate high-dimensional integrals motivated by Ising model in quantum physics. In contrast to…
Tensor product approach to quantum control
Diego Quiñones Valles, Sergey Dolgov, Dmitry Savostyanov
In this proof-of-concept paper we show that tensor product approach is efficient for control of large quantum systems, such as Heisenberg spin wires, which are essential for emergi…