This algorithm constructs optimal maps for multi-parametric programming, suggesting significant improvements over existing methods.
In this paper, we present a new paradigm to construct an optimal map of a multi-parametric quadratic or linear program, based on random sampling. Probabilistic guarantees for coverage of the region over which the map is constructed are provided in terms of user-defined coverage and confidence parameters. Extensive simulations show that the proposed Multi-Parametric Randomized algorithm (MPR) outperforms state-of-the-art competitors.
No takes yet. Share an insight, caveat, or question.
Falsone et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: