Paper Info
Title
Non-linearity and Non-convexity in Optimal Knots Selection for Sparse Reduced Data.
Abstract
The problem of fitting sparse reduced data in arbitrary Euclidean space is discussed in this work. In our setting, the unknown interpolation knots are determined upon solving the corresponding optimization task. This paper outlines the non-linearity and non-convexity of the resulting optimization problem and illustrates the latter in examples. Symbolic computation within Mathematica software is used to generate the relevant optimization scheme for estimating the missing interpolation knots. Experiments confirm the theoretical input of this work and enable numerical comparisons (again with the aid of Mathematica) between various schemes used in the optimization step. Modelling and/or fitting reduced sparse data constitutes a common problem in natural sciences (e.g. biology) and engineering (e.g. computer graphics).
Year
Venue
Field
2017
CASC
Discrete mathematics,Mathematical optimization,Computer science,Interpolation,Symbolic computation,Algorithm,Euclidean space,Software,Knot (unit),Computer graphics,Optimization problem,Sparse matrix
DocType
Citations 
PageRank 
Conference
1
0.39
References 
Authors
7
2
Name
Order
Citations
PageRank
Ryszard Kozera116326.54
Lyle Noakes214922.67