Add to ORKGThe problem of data approximation is of great interest. There are a lot of approaches to solve this problem. One of them is a polynomial spline approximation. In this paper we propose a new algorithm for polynomial spline approximation based on nonsmooth optimization techniques. Numerical experiments using this algorithm have been carried out. The results are presented and discussed.E
Data and function approximation is fundamental in application domains like path planning or signal p...
In a series of three articles, spline approximation is presented from a geodetic point of view. In p...
Least squares polynomial splines are an effective tool for data fitting, but they may fail to preser...
The problem of data approximation is of great interest. There are a lot of approaches to solve this ...
The problem of data approximation is of great interest. There are a lot of approaches to solve this ...
International audienceIn this article, we address the problem of approximating data points by C1-smo...
International audienceIn this article, we address the problem of approximating data points by C1-smo...
International audienceIn this article, we address the problem of approximating data points by C1-smo...
Abstract- Spline approximation is often preferred over polynomial approximation. They require less n...
This paper describes the approximation of discrete data using splines. The approximation method is a...
The classical Remez algorithm was developed for constructing the best polynomial approximations for ...
The classical Remez algorithm was developed for constructing the best polynomial approximations for ...
It is often important in practice to obtain approximate representations of physical data by relative...
While polynomial regression models on a one-dimensional interval have received broad attention in op...
Data and function approximation is fundamental in application domains like path planning or signal p...
Data and function approximation is fundamental in application domains like path planning or signal p...
In a series of three articles, spline approximation is presented from a geodetic point of view. In p...
Least squares polynomial splines are an effective tool for data fitting, but they may fail to preser...
The problem of data approximation is of great interest. There are a lot of approaches to solve this ...
The problem of data approximation is of great interest. There are a lot of approaches to solve this ...
International audienceIn this article, we address the problem of approximating data points by C1-smo...
International audienceIn this article, we address the problem of approximating data points by C1-smo...
International audienceIn this article, we address the problem of approximating data points by C1-smo...
Abstract- Spline approximation is often preferred over polynomial approximation. They require less n...
This paper describes the approximation of discrete data using splines. The approximation method is a...
The classical Remez algorithm was developed for constructing the best polynomial approximations for ...
The classical Remez algorithm was developed for constructing the best polynomial approximations for ...
It is often important in practice to obtain approximate representations of physical data by relative...
While polynomial regression models on a one-dimensional interval have received broad attention in op...
Data and function approximation is fundamental in application domains like path planning or signal p...
Data and function approximation is fundamental in application domains like path planning or signal p...
In a series of three articles, spline approximation is presented from a geodetic point of view. In p...
Least squares polynomial splines are an effective tool for data fitting, but they may fail to preser...