### Abstract

The problem of interpolating or approximating a given set of data points obtained empirically by measurement frequently arises in a vast number of scientific and engineering applications, for example, in the design of airplane bodies, cross sections of ship hull and turbine blades, in signal processing or even in less classical things like flow lines and moving boundaries from chemical processes. All these areas require fast, efficient, stable and flexible algorithms for smooth interpolation and approximation to such data. Given a set of empirical data points in a plane, there are quite a few methods to estimate the curve by using only these data points. In this paper, we consider using polynomial least squares approximation, polynomial interpolation, cubic spline interpolation, exponential spline interpolation and interpolatory subdivision algorithms. Through the investigation of a lot of examples, we find a 'reasonable good' fitting curve to the data.

Original language | English (US) |
---|---|

Pages (from-to) | 153-166 |

Number of pages | 14 |

Journal | Applied Mathematics and Computation |

Volume | 108 |

Issue number | 2-3 |

DOIs | |

State | Published - Feb 15 2000 |

### Keywords

- Approximation
- Minimal energy curve
- Spline
- Subdivision algorithms

### ASJC Scopus subject areas

- Computational Mathematics
- Applied Mathematics

## Fingerprint Dive into the research topics of 'Approximation of minimum energy curves'. Together they form a unique fingerprint.

## Cite this

*Applied Mathematics and Computation*,

*108*(2-3), 153-166. https://doi.org/10.1016/S0096-3003(99)00012-0