Research Article | Open Access
Feng-Gong Lang, Xiao-Ping Xu, "Error Analysis for a Noisy Lacunary Cubic Spline Interpolation and a Simple Noisy Cubic Spline Quasi Interpolation", Advances in Numerical Analysis, vol. 2014, Article ID 353194, 8 pages, 2014. https://doi.org/10.1155/2014/353194
Error Analysis for a Noisy Lacunary Cubic Spline Interpolation and a Simple Noisy Cubic Spline Quasi Interpolation
We mainly present the error analysis for two new cubic spline based methods; one is a lacunary interpolation method and the other is a very simple quasi interpolation method. The new methods are able to reconstruct a function and its first two derivatives from noisy function data. The explicit error bounds for the methods are given and proved. Numerical tests and comparisons are performed. Numerical results verify the efficiency of our methods.
Cubic spline, as the most commonly used spline in practice, is a fundamental approximation tool [1–11]. Nowadays, it has been widely used in many fields such as numerical analysis, computer aided geometric design, mathematical modeling, and engineering problems. Essentially, cubic spline is a twice differentiable piecewise cubic polynomial defined over a partitioned interval.
Mathematically, cubic spline interpolation is often introduced as follows. Let be a function defined over , let be a set of given function data at the nodes and let be two boundary derivatives. Then, there exists a unique cubic spline satisfying
However, we often meet two troubles in the practical applications of cubic spline interpolation. The first trouble is that we cannot obtain the precise function values in (1). They generally involve some unavoidable measurement noise. The second trouble is that it often lacks the boundary derivatives in (3).
To deal with the troubles, in this paper, we give two new effective cubic spline based methods for reconstructing , , and from the given noisy data where is the measurement noise. The first one is a noisy lacunary interpolation method (Method I) and the second one is a very simple noisy quasi interpolation method (Method II). The error bounds of the methods, which have not been studied before and are important and useful for the users of cubic spline, are mainly studied in this paper.
We organize the remainder of this paper as follows. In Section 2, we present some useful preliminaries; in Section 3, we give the new methods; in Section 4, we present the theoretical results of the errors; in Section 5, we perform some numerical tests to verify the error analysis; finally, we conclude this paper in Section 6.
2.1. Cubic B-Splines
We assume that the nodes in (2) are equidistant because this case is very common in practice. The nodes produce a uniform partition for with mesh size . The dimension of the cubic spline space over is . The corresponding cubic B-splines are given below [4–8]. For , let The other six B-splines , , , , , and are generated by the translation, where They are linearly independent, nonnegative, and locally supported. Moreover, The values of , , and at the nodes are listed in Table 1.
2.2. Approximate Boundary Derivatives
Using two-point numerical differentiation formula, we have Similar results can be obtained by using three-point and five-point numerical differentiation formulae. See Tables 2 and 3, where , , , , , and and and represent the computational truncated errors to and . They arise from the used numerical differentiation formulae and the above-mentioned measurement noise.
3. Two New Methods
3.1. Method I
We study the following noisy lacunary cubic spline interpolation (NLCSI) problem. We hope to find a cubic spline satisfying To make the NLCSI problem uniquely solvable, it requires using two approximate boundary derivatives in Section 2.2. Obviously, there also exists a unique noisy lacunary cubic spline satisfying
Let be the cubic spline determined by (11), where the unknown coefficients can be obtained by solving the linear system followed from Table 1. Furthermore, we can use and to approximate and , respectively.
3.2. Method II
By using the given function data, we can directly get a cubic spline where and . We can also use , , and to approximate , , and , respectively.
The method is very simple and effective method for noisy data because it avoids using approximate boundary derivatives and also avoids solving the linear system (12).
4. Main Results
4.1. Error Analysis for Method I
We denote (12) by .
Lemma 1. is invertible and .
Proof. Add column one to column three and also add column to column , and we get a strictly diagonally dominant matrix Obviously, is invertible and , where We have Hence, we have .
Lemma 2. Consider .
Proof. Consider .
Lemma 3. Consider , , and .
Proof. Because of property (8), we only need to check them over a typical subinterval . By differentiating (6), for a general , we have All of them are locally supported over four adjacent subintervals. (i)By the nonnegativity and partition of unity of cubic B-splines, for , we have (ii)For , we have (iii)For , we have
Theorem 5. Let be the noisy lacunary cubic spline interpolant of determined by (11). Then one has
4.2. Error Analysis for Method II
Lemma 6. Let be the noisy cubic spline quasi interpolant of determined by (13). Then for , one has
Theorem 7. Let be the noisy cubic spline quasi interpolant of determined by (13). Then we have
Proof. We first prove (37). is a cubic spline; hence is a piecewise continuous linear function over with respect to the partition . Let be the piecewise linear interpolant to with respect to . For , let and be the restriction of and over . Then we have
For , by (38) and (33), we get
Hence, for all , we have
Moreover, by the piecewise linear polynomial interpolation theory [1, 3, 7], for all , we have
Then (37) follows immediately from (40) and (41).
Next, we prove (36). For , let be the restriction of over . Then for , by (32) and (37), we have
Finally, we prove (35). For every subinterval , , we give They are very useful in cubic Hermite interpolation, and we also have Let be the restriction of over ; then it can also be written as Let be the cubic Hermite interpolant of over ; then for , by using (31), (32), (44), (45), and (46), we have By the triangle inequality, we get (35).
5. Numerical Tests and Discussions
5.1. Numerical Tests
In this section, we perform numerical tests by Matlab. The following examples are considered.
In every numerical test, the mesh size and the measurement noise bound are both given. Because the measurement noises are random, we let , where are random numbers and satisfy .
In Tables 5 and 6, Methods I-1, I-2, and I-3 represent Method I with two-point, three-point, and five-point approximate boundary derivatives, respectively. CSM represents the cubic spline method in . , , and are the maximum absolute error of the function, the first order derivative, and the second order derivative, respectively.