preprint · arXiv (Cornell University) · 2022
Convex and Nonconvex Sublinear Regression with Application to Data-driven Learning of Reach Sets
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Abstract
We consider estimating a compact set from finite data by approximating the support function of that set via sublinear regression. Support functions uniquely characterize a compact set up to closure of convexification, and are sublinear (convex as well as positive homogeneous of degree one). Conversely, any sublinear function is the support function of a compact set. We leverage this property to transcribe the task of learning a compact set to that of learning its support function. We propose two algorithms to perform the sublinear regression, one via convex and another via nonconvex programming. The convex programming approach involves solving a quadratic program (QP). The nonconvex programming approach involves training a input sublinear neural network. We illustrate the proposed methods via numerical examples on learning the reach sets of controlled dynamics subject to set-valued input uncertainties from trajectory data.
Cite this paper
Haddad, S., & Halder, A. (2022). Convex and Nonconvex Sublinear Regression with Application to Data-driven Learning of Reach Sets. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2210.01919
Haddad, Shadi, and Abhishek Halder. “Convex and Nonconvex Sublinear Regression with Application to Data-driven Learning of Reach Sets.” arXiv (Cornell University), 2022, https://doi.org/10.48550/arxiv.2210.01919.
Haddad, Shadi, and Abhishek Halder. 2022. “Convex and Nonconvex Sublinear Regression with Application to Data-driven Learning of Reach Sets.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2210.01919.
Haddad, S. and Halder, A. (2022) ‘Convex and Nonconvex Sublinear Regression with Application to Data-driven Learning of Reach Sets’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2210.01919.
@misc{haddad2022convex,
title = {{Convex and Nonconvex Sublinear Regression with Application to Data-driven Learning of Reach Sets}},
author = {Haddad, Shadi and Halder, Abhishek},
journal = {arXiv (Cornell University)},
year = {2022},
doi = {10.48550/arxiv.2210.01919}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.