.png){kind=icon}
.png){kind=icon}
Return
MPR: A novel randomized algorithm for multi-parametric programming
.png){kind=icon}
OA
AI
DOI：10.1016/j.automatica.2026.112873.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multi-parametric programming
Randomized algorithms
Explicit model predictive control
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.9
Papers:
1.2W
Citations:
5.2W