Randomized progressive iterative approximation for B-spline curve and surface fittings

Nian-Ci Wu and Cheng-Zhi Liu

Applied Mathematics and Computation, 2024, vol. 473, issue C

Abstract: For large-scale data fitting, the least-squares progressive iterative approximation is a widely used method in many applied domains because of its intuitive geometric meaning and efficiency. In this work, we present a randomized progressive iterative approximation (RPIA) for the B-spline curve and surface fittings. In each iteration, RPIA locally adjusts the control points according to a random criterion of index selections. The difference for each control point is computed concerning the randomized block coordinate descent method. From geometric and algebraic aspects, the illustrations of RPIA are provided. We prove that RPIA constructs a series of fitting curves (resp., surfaces), whose limit curve (resp., surface) can converge in expectation to the least-squares fitting result of the given data points. Numerical experiments are given to confirm our results and show the benefits of RPIA.

Keywords: Data fitting; Progressive iterative approximation; Least-squares; Randomized algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:473:y:2024:i:c:s0096300324001413

DOI: 10.1016/j.amc.2024.128669

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