State-constrained Optimization Problems under Uncertainty: A Tensor Train Approach (2023)
Attributed to:
Overcoming the curse of dimensionality in dynamic programming by tensor decompositions
funded by
EPSRC
Abstract
No abstract provided
Bibliographic Information
Digital Object Identifier: http://dx.doi.org/10.48550/arxiv.2301.08684
Publication URI: https://arxiv.org/abs/2301.08684
Type: Preprint