Polynomial and spline approximation by quadratic programming-Reference-Cited by-同舟云学术

Polynomial and spline approximation by quadratic programming

Published:1969-07 Issue:7 Volume:12 Page:379-380
ISSN:0001-0782
Container-title:Communications of the ACM
language:en
Short-container-title:Commun. ACM

Author:

Amos D. E.¹,Slater M. L.¹

Affiliation:

1. Sandia Laboratory, Albuquerque, New Mexico

Abstract

The problem of approximation to a given function, or of fitting a given set of data, where the approximating function is required to have certain of its derivatives of specified sign over the whole range of approximation, is studied. Two approaches are presented, in each of which quadratic programming is used to provide both the constraints on the derivatives and the selection of the function which yields the best fit. The first is a modified Berstein polynomial scheme, and the second is a spline fit.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/363156.363163

Reference2 articles.

1. BOOT J. C. G. Quadratic Programming. Rand McNally Chicago 1964. BOOT J. C. G. Quadratic Programming. Rand McNally Chicago 1964.

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CCS: A software framework to generate two-body potentials using Curvature Constrained Splines;Computer Physics Communications;2021-01

2. A Convex Optimization Approach to Curve Fitting with B-Splines;IFAC Proceedings Volumes;2011-01

3. References;Handbook of Splines;1999

4. Splines in Statistics;Journal of the American Statistical Association;1983-06

5. SPLINES IN STATISTICS;J AM STAT ASSOC;1983