Research Article | Open Access

Volume 2021 |Article ID 5556771 | https://doi.org/10.1155/2021/5556771

Yeqing Yi, Zixuan Tang, Chengzhi Liu, "Progressive Iterative Approximation for Extended B-Spline Interpolation Surfaces", Mathematical Problems in Engineering, vol. 2021, Article ID 5556771, 10 pages, 2021. https://doi.org/10.1155/2021/5556771

# Progressive Iterative Approximation for Extended B-Spline Interpolation Surfaces

Revised23 Mar 2021
Accepted13 Apr 2021
Published27 Apr 2021

#### Abstract

In order to improve the computational efficiency of data interpolation, we study the progressive iterative approximation (PIA) for tensor product extended cubic uniform B-spline surfaces. By solving the optimal shape parameters, we can minimize the spectral radius of PIA’s iteration matrix, and hence the convergence rate of PIA is accelerated. Stated numerical examples show that the optimal shape parameters make the PIA have the fastest convergence rate.

#### 1. Introduction

Data interpolation plays important roles in scientific research and engineering applications. How to solve interpolation curves/surfaces efficiently has been one of the most popular topics in computer-aided geometric design (see ). Oftentimes, one has to solve a linear system to obtain the interpolation curves or surfaces. Efficient and accurate algorithms are required to guarantee the computational efficiency. For small-scale systems, direct methods are typically the preferred choices. However, for large-scale systems, it becomes necessary to employ iterative methods to obtain the solutions. In recent years, an iterative method, namely, progressive iterative approximation (PIA), has attracted a lot of attention and has become a very hot research area. The PIA stands out because it has the advantages of clear geometric meaning, stable convergence, simple iterative format, local modification, and so on. Furthermore, it avoids to solve a linear system directly. For more details about PIA, we refer the readers to read a recent survey .

Despite the fact that the PIA offers many advantages, there is a disadvantage, that is, slow rate of convergence. To overcome this limitation and further improve the computational efficiency, a great deal of acceleration techniques have been conducted. Examples of such approaches include  and a lot of literatures therein.

The emergence of blending bases with shape parameters has enriched the theories and methods of geometric modeling [1, 1517]. Due to the flexibly in shape adjustment, splines with shape parameters have drawn much attention for decades and a large number of splines with shape parameters were exploited (see, for example, ). Very often, the aim of shape parameters is to adjust the shapes of splines, while in , the introduction of shape parameters is to speed up the convergence rate of PIA. In that paper, the eigenvalues of the collocation matrix were expressed explicitly, and hence the optimal shape parameters were solved to make the PIA have the fastest convergence rate. Based on this conclusion, we further study the PIA format for tensor product extended cubic uniform B-spline surfaces, which is an extension of the PIA for the classic bicubic uniform B-spline curves. By solving the optimal shape parameters, the convergence rate of PIA is accelerated, and thus the computational efficiency of data interpolation can be improved.

The rest of this paper is organized as follows. After recapping the definition of the extended cubic uniform B-spline surfaces with shape parameters, we exploit the PIA format for extended cubic uniform B-spline surfaces in Section 2. In Section 3, we study the optimal shape parameters to make the PIA have the fastest convergence rate. Some numerical examples are given to illustrate the acceleration effect in Section 4. Finally, we give some concluding remarks in Section 5.

#### 2. PIA for Extended Cubic Uniform B-Spline Surface

##### 2.1. Extended Cubic Uniform B-Spline Surface

We begin with the definition of the extended cubic uniform B-spline basis with a shape parameter.

Definition 1. (see ). For , the extended cubic uniform B-spline basis (abbr. -B-spline basis) iswhere is the so-called shape parameter.
The -B-spline basis has the properties of non-negativity and symmetry, and it will degenerate into the classic cubic B-spline basis if .

Definition 2. (see ). Given knot vectors and such that and . Let be the control points, and Let and be the -B-spline bases defined as in (1). Then, for , and , , we can define extended cubic uniform B-spline patches with shape parameters and asAll these patches comprise an entire extended cubic uniform B-spline surface (abbr. -B-spline surface):Due to the degeneracy property of the -B-spline basis, it is easy to verify that the -B-spline surface will degenerate into the classic bicubic B-spline surface if .
If we want the -B-spline surface to interpolate the boundary control points, we have to add several control vertices according to

##### 2.2. PIA

Let and be the -B-spline bases defined as in (1). Given a set of organized data points to be interpolated, we assign a pair of parameters to the th point , where and .

Firstly, the points as well as these added points (4) are interpreted as the control points of a -B-spline surface. Therefore, we can obtain the initial approximate interpolation curvewhere .

Secondly, letbe the adjusting vectors of the control points. Then, we can adjust the control points according to

Suppose that we have obtained the th, , approximate interpolation surface ; then, the th approximate interpolation surface can be defined aswhere

Therefore, we obtain a sequence of approximate interpolation surfaces . The initial surface is said to have the PIA property if the limit of interpolates the points . It was shown in  that tensor product surfaces generated by normalized and totally positive bases have the PIA property. We note in  that the -B-spline basis is normalized and totally positive; therefore, the initial -B-spline surface has the PIA property.

Let

Then, equation (9) can be written aswhere is the Kronecker product, is the identity matrix, and and are the collocation matrices resulting from the -B-spline basis; in detail,

We refer to the iterative format (12) as the PIA format. The matrix in (12) is the iteration matrix. It is well known that the PIA converges if and only if the spectral radius of the iteration matrix is less than 1. Moreover, the smaller the spectral radius is, the faster the PIA converges. According to (13), the spectral radius is a function of the shape parameters and . This indicates that we can minimize the spectral radius by selecting optimal shape parameters and , and hence the convergence of PIA can be improved. We will discuss the optimal shape parameters and in the following section.

Finally, we give some notations. The spectral radius of a matrix is denoted by , the eigenvalues of are denoted by , and the smallest eigenvalue of a matrix is denoted by .

#### 3. Optimal Shape Parameters

In order to make the PIA have the fastest convergence rate, we have to solve the optimal shape parameters and that minimize the spectral radius of PIA’s iteration matrix, i.e.,

Lemma 1 (see ). Suppose that and are square matrices of size and , respectively. Let and be the eigenvalues of and , respectively. Then, the eigenvalues of are .

Lemma 2 (see ). Let be an collocation matrix resulting from the -B-spline basis. Then, the eigenvalues of areBy direct deduction, we have the following corollary.

Corollary 1. Let be an collocation matrix resulting from the -B-spline basis. Then,(1)The eigenvalues of are distributed at the interval .(2)Given , the smallest eigenvalue of is .(3) decreases as increases and reaches minimum when .

Theorem 1. Let and be the collocation matrices defined as in (13). For fixed , the spectral radius of the iteration matrix of PIA isThe PIA has the fastest convergence rate when , and in such case, the spectral radius is

Proof. According to Corollary 1, for ; , we have , so is the product of and , i.e., . Combined with Lemma1, we haveFrom Corollary 1, and minimize at and , respectively. By substituting into (16), the result (17) follows straightforwardly.

#### 4. Numerical Examples

In this section, several numerical examples are presented to assess the effectiveness of the optimal shape parameters. All experiments were performed by Matlab R2012b.

Let be the points to be interpolated, and let be the th approximate interpolation -B-spline surface. Then, the interpolation error of can be defined aswhere is the Euclidean norm.

Example 1. Consider the data interpolation of points sampled from the peaks functionin the following way:

Example 2. Consider the data interpolation of 16 points: , , , , , , , , , , , , , , , .

Example 3. Consider the data interpolation of points sampled from the functionat .

Example 4. Consider the data interpolation of points sampled from the functionat .
The PIA for -B-spline surfaces with different and is employed to interpolate the points in Examples 14. It should be pointed out that the PIA for -B-spline surfaces will degenerate into the PIA for the classic bicubic B-spline surfaces if .
As an illustration, we show in Figure 1 the spectral radii of PIA’s iteration matrices with different shape parameters in Examples 14. In Table 1, we list the spectral radii of PIA’s iteration matrices. For convenience, the notation in Table 1 and the subsequent tables represents the values of the shape parameters and . We can see from Figure 1 and Table 1 that the spectral radii of iteration matrices are less than 1 for any and minimize at , and hence the PIA converges for and has the fastest convergence rate when . Those results coincide with the conclusions in Theorem 1. Thus, for the optimal shape parameters and , the convergence rate of PIA for -B-spline surfaces would achieve a great acceleration compared with that for the classic bicubic B-spline surfaces.
Given interpolation errors, we list in Table 2 the number of required iterations when we test Example 1 with different shape parameters. It is evident from Table 2 that under the requirement of the same precision, the number of iterations of PIA with is the smallest. In Tables 3 and 4 , we list the interpolation errors of Examples 24 obtained by implementing the PIA for -B-spline surfaces with different and . We can see that with the same iterations, the interpolation errors obtained by the PIA with are the smallest. Figures 29 display the -B-spline surfaces with different shape parameters when we employ the PIA to interpolate the data given in Examples 14.
All these numerical results shown in these tables and figures indicate that the PIA for -B-spline surfaces has the fastest convergence rate when .

 Example Example 1 0.8400 0.8000 0.7714 0.7500 0.7500 0.6875 0.6875 0.6429 0.6094 Example 2 0.9518 0.9232 0.9028 0.8875 0.8777 0.8209 0.8209 0.7733 0.7376 Example 3 0.9599 0.9332 0.9142 0.8999 0.8888 0.8332 0.8332 0.7856 0.7499 Example 4 0.9600 0.9333 0.9142 0.9000 0.8888 0.8333 0.8333 0.7857 0.7500
 1e − 03 43 30 26 24 21 16 17 12 12 1e − 04 76 49 40 36 33 24 24 19 16 1e − 05 114 72 58 50 47 32 33 26 22 1e − 06 152 96 77 66 63 42 43 33 28 1e − 07 191 122 97 84 78 52 54 41 35 1e − 08 232 147 118 102 94 63 65 49 42 1e − 09 273 174 139 120 110 73 76 57 49 1e − 10 317 200 160 139 126 84 86 66 56
 0 2.20e − 01 2.00e − 01 1.86e − 01 1.75e − 01 1.81e − 01 1.56e − 01 1.56e − 01 1.33e − 01 1 1.52e − 02 1.22e − 02 1.10e − 02 1.06e − 02 8.87e − 03 6.73e − 03 6.73e − 03 3.78e − 03 2 7.33e − 03 5.89e − 03 4.61e − 03 3.55e − 03 5.00e − 03 3.35e − 03 3.35e − 03 2.53e − 03 3 5.20e − 03 3.80e − 03 2.86e − 03 2.21e − 03 2.82e − 03 1.67e − 03 1.67e − 03 1.02e − 03 4 2.38e − 03 1.61e − 03 1.15e − 03 8.64e − 04 1.07e − 03 5.69e − 04 5.69e − 04 2.93e − 04 5 9.60e − 04 6.00e − 04 4.09e − 04 2.97e − 04 3.64e − 04 1.72e − 04 1.72e − 04 7.52e − 05 10 6.54e − 06 2.80e − 06 1.46e − 06 8.65e − 07 1.08e − 06 2.85e − 07 2.85e − 07 5.84e − 08 15 3.98e − 08 1.16e − 08 4.52e − 09 2.15e − 09 2.89e − 09 4.19e − 10 4.19e − 10 4.14e − 11 20 2.41e − 10 4.78e − 11 1.39e − 11 5.23e − 12 7.69e − 12 6.10e − 13 6.10e − 13 2.89e − 14
 Example 3 Example 4 0 1.48e − 03 1.24e − 03 9.27e − 04 1.35e − 03 1.12e − 03 8.42e − 04 1 4.87e − 05 3.39e − 05 1.91e − 05 4.71e − 05 3.27e − 05 1.84e − 05 2 1.94e − 05 1.12e − 05 4.75e − 06 1.88e − 05 1.09e − 05 4.58e − 06 3 9.67e − 06 4.67e − 06 1.48e − 06 9.37e − 06 4.52e − 06 1.43e − 06 4 5.41e − 06 2.18e − 06 5.17e − 07 5.24e − 06 2.11e − 06 5.00e − 07 5 3.70e − 06 1.24e − 06 2.21e − 07 3.59e − 06 1.20e − 06 2.14e − 07 10 6.22e − 07 8.39e − 08 3.55e − 09 6.04e − 07 8.13e − 08 3.43e − 09 20 3.37e − 08 7.35e − 10 1.76e − 12 3.27e − 08 7.11e − 10 1.69e − 12 50 2.09e − 11 1.21e − 13 3.15e − 16 1.67e − 11 7.89e − 14 1.99e − 15

#### 5. Conclusion

In this paper, we have exploited the PIA format for -B-spline surfaces. Due to the introduction of shape parameters, we can make the PIA have the fastest convergence rate by solving the optimal shape parameters, while the amount of calculation does not increase. Therefore, it inherits the merits of the PIA for the classic bicubic B-spline surfaces, e.g., simple iterative scheme, stable convergence, clear geometric meaning, local modification, etc. More importantly, the computational efficiency of data interpolation is improved by accelerating the convergence rate.

#### Data Availability

The data are included within tha article.

#### Conflicts of Interest

The authors declare that they have no conflicts of interest.

#### Acknowledgments

This research was supported by the Natural Science Foundation of Hunan Province (grant no. 2020JJ5267) and Scientific Research Funds of Hunan Provincial Education Department (grant nos. CX20201192 and 19B301).

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