Journal of Applied Mathematics

Volume 2015, Article ID 572768, 15 pages

http://dx.doi.org/10.1155/2015/572768

## Positivity Preserving Interpolation Using Rational Bicubic Spline

^{1}Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak Darul Ridzuan, Malaysia^{2}School of Mathematical Sciences, Universiti Sains Malaysia (USM), 11800 Minden, Penang, Malaysia^{3}College of Arts and Sciences, Universiti Utara Malaysia (UUM), 06010 Sintok, Kedah, Malaysia

Received 12 February 2015; Accepted 26 February 2015

Academic Editor: Juan Manuel Peña

Copyright © 2015 Samsul Ariffin Abdul Karim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper discusses the positivity preserving interpolation for positive surfaces data by extending the *C*^{1} rational cubic spline interpolant of Karim and Kong to the bivariate cases. The partially blended rational bicubic spline has 12 parameters in the descriptions where 8 of them are free parameters. The sufficient conditions for the positivity are derived on every four boundary curves network on the rectangular patch. Numerical comparison with existing schemes also has been done in detail. Based on Root Mean Square Error (RMSE), our partially blended rational bicubic spline is on a par with the established methods.

#### 1. Introduction

Shape preserving interpolation is a process that involves some mathematical derivation to visualize the two-dimensional (2D) or three-dimensional (3D) data sets in the form of piecewise curves or surfaces that satisfies some degree of smoothness. Notably the shape preserving method will be able to maintain the original shape of the data sets. For example, if the given data is positive, then the final resulting curves or surface will be positive everywhere and it should be visual pleasing for the computer graphics displays. Usually the collected data from some laboratory experiment may contain little unwanted noise. Common cubic spline interpolation is not able to produce the shape preserving interpolation for positive, monotone and convexity of the data sets. Even though the interpolating curves are very smooth, that is, with continuity, there may exist some uncharacteristic behavior such as wiggle that will result in the negative values along the whole intervals. This unwanted flaw must be eliminated before the data are processed by the user. Therefore for the shape preserving interpolation and practical designing purposes, cubic spline and cubic Hermite spline are not recommended. Thus many researchers have proposed the idea of how to preserve the positive data by using some rational spline function with or continuity.

Some literature studies for positivity preserving are as follows. Abbas et al. [1, 2] have discussed the positivity preserving by using partially blended rational bicubic spline function and rational bicubic spline function, respectively. Asim and Brodlie [3], Butt and Brodlie [4], and Brodlie et al. [5] discussed the use of bicubic spline for positivity preserving by inserting one or two extra knots in the region or interval where the negativity of the interpolant is found. Asim et al. [6] have studied the use of modified quadratic Shepard method for positivity preserving of large data sets. Duan et al. [7] have proposed new bivariate rational interpolation based on function values. They utilized the rational cubic spline of the forms of cubic numerator and linear denominator. Meanwhile, Duan et al. [8, 9] investigated the bounded property, point control, and shape control of the bivariate rational cubic spline with linear denominator. Goodman et al. [10] discussed the constrained interpolation by using rational cubic with cubic denominator. Goodman [11] summarized all shape preserving criteria for the univariate cases. Hussain and Sarfraz [12] and M. Z. Hussain and M. Hussain [13] also studied the positivity preserving by utilizing the rational cubic spline of the forms cubic/cubic and cubic/quadratic, respectively. In the study by M. Z. Hussain and M. Hussain [13], there are no free parameters for the refining processes. M. Z. Hussain and M. Hussain [14] have developed the surface construction scheme for positivity preserving of positive scattered data arranged over triangular grids. They utilized the rational cubic spline of Tian et al. [15] without any free parameters. Hussain et al. [16] have developed shape preserving surfaces for positive and convex data using rational biquadratic splines. The degree of smoothness attained is as can clearly be seen from their graphical results. Ibraheem et al. [17] develop a positivity preserving scheme by using trigonometric rational functions which will increase the computation times. Karim and Kong [18] have proposed new rational cubic spline of the form cubic/quadratic with three parameters for local control of interpolating function. Positivity preserving by using this rational cubic spline has been addressed in the study by Karim and Kong [19]. Liu et al. [20] have studied the positivity and monotonicity preserving by using new rational biquartic spline by utilizing the extension cubic Ball basis function with two parameters. Saaban et al. [21] studied the positivity preserving by using quintic triangular Bezier patches. The surfaces are constructed by using convex combination of quintic triangular Bezier patches. They use the real data collected from the rainfall at various stations in West Peninsular of Malaysia. Sarfraz et al. [22] also have developed rational cubic spline scheme for positivity preserving of 3D data. Their scheme has 8 parameters without any free parameter. Furthermore Abbas et al. [1] indicated that Sarfraz et al. [22] works do not produce positive surfaces. The surfaces may only have continuity. Bastian-Walther and Schmidt [23] discussed the range restricted data interpolation for scattered data by using rational cubic spline of Gregory [24]. Wu et al. [25] studied the positivity preserving data approximation by using compactly supported radial basis functions (CSRBFs). Their scheme requires the solving of optimization problems in order to calculate the parameters that will guarantee that the positivity of the data is preserved. Peng et al. [26] have used the rational cubic spline of Hussain and Sarfraz [12] to construct the positive rational cubic Bezier interpolant for scattered positive data. Their methods require the modification of the first derivatives if the positivity of the rational interpolant is not met. The bicubic Hermite spline [27] interpolation cannot preserve the positivity of the surface data sets, since there maybe exist some negative values on the constructed surfaces. This paper is a continuation of our previous works. The univariate rational cubic spline interpolation in the studies by Karim and Kong [18, 19] is extended to the bivariate cases. Positivity preserving interpolation is our main object in this paper. We compare the performance between our rational bicubic spline and that of the work of Abbas et al. [1] by using Root Mean Square Error (RMSE).

We identify several advantages of our proposed partially blended rational bicubic spline interpolation for positive data as follows.(i)Our schemes have 12 parameters and 8 of them are free parameters; meanwhile, in the study by M. Z. Hussain and M. Hussain [13] the bivariate spline has 8 parameters and none of them are free parameters.(ii)Our schemes are applicable for both equally or unequally spaced data; meanwhile, the rational cubic spline of Duan et al. [7–9] requires that the data be equally spaced.(iii)The scheme can also be used whether the first derivative is given or not. Meanwhile, the works of Duan et al. [7, 9] require the true function values without the first derivative values.(iv)Our rational bicubic spline is on a par with the works of Abbas et al. [1, 2] and Hussain and Sarfraz [12]. Furthermore the presented scheme is easy to use and not involving any trigonometric functions compared with the work of Ibraheem et al. [17].(v)When , our partially blended rational bicubic spline is reduced to the work of M. Z. Hussain and M. Hussain [13].(vi)The partially blended rational bicubic spline is unique and the free parameters in its description provide the flexibility in controlling the final shape of the positive surface data sets.(vii)Furthermore based on Root Mean Square Error (RMSE) our partially blended rational bicubic spline gives numerical results as good as the numerical results from the study by Abbas et al. [1].The remainder of the paper is organized as follows. Section 2 is devoted to the derivative estimation for 2D and 3D data by using Arithmetic Mean Method (AMM). Meanwhile, Section 3 reviews the univariate of rational cubic spline initiated by Karim and Kong [18, 19]. Positivity preserving for 2D data also is discussed in this section. The partially bicubic rational cubic spline interpolation is discussed in Section 4. Section 5 discusses the positivity preserving for 3D positive data sets by using the constructed partially bicubic rational cubic spline interpolation with numerical demonstrations with comparison to the existing schemes. Finally a summary and conclusions are given in Section 6.

#### 2. Derivative Estimation

The first derivative that depends on the original data can be chosen by using Arithmetic Mean Method (AMM). The details are as follows:

*For 2D Data*

At the interior points, , , the values of are given as

*For 3D data *where

#### 3. Review of Rational Cubic Spline Interpolant

In this section the rational cubic spline of Karim and Kong [18, 19] is discussed briefly before it extends to the bivariate cases.

Given the set of data points such that , let , , and , where . For , ,whereThe rational function in (5) satisfies continuity as follows:It can be shown that the unknowns , , are given as follows: where denotes derivative with respect to and denotes the derivative value which is given at the knot , . The parameters are chosen such that , . When , , the rational cubic interpolant in (5) is reduced to cubic Hermite spline [19].

*Positivity Preserving.* For the strictly positive set of data , , ,Karim and Kong [19] have developed the following results for strictly positive data sets given in (9).

Theorem 1. *For strictly positive data defined in (9), the rational cubic interpolant defined over the interval is positive if in each subinterval , , the involving parameters , , and satisfy the following sufficient conditions:*

*3.1. Numerical Demonstrations*

*In this section the positivity shape preserving using rational cubic spline of Karim and Kong [19] is discussed. The positive data is taken from the study by Hussain et al. [28].*

*Example 2. *
See Tables 1 and 2 and Figure 1.