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A Spectral Collocation Method for Nonlinear Fractional Boundary Value Problems with a Caputo Derivative

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Abstract

In this paper, we consider the nonlinear boundary value problems involving the Caputo fractional derivatives of order \(\alpha \in (1,2)\) on the interval (0, T). We present a Legendre spectral collocation method for the Caputo fractional boundary value problems. We derive the error bounds of the Legendre collocation method under the \(L^2\)- and \(L^\infty \)-norms. Numerical experiments are included to illustrate the theoretical results.

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Correspondence to Zhongqing Wang.

Additional information

The research of Zhongqing Wang was supported by the National Natural Science Foundation of China (No. 11571238). The research of Lilian Wang was partially supported by Singapore MOE AcRF Tier 1 Grants (RG15/12 and RG27/15), and Singapore MOE AcRF Tier 2 Grant (MOE 2013-T2-1-095, ARC 44/13).

Appendix

Appendix

In this appendix, we verify the existence and uniqueness of scheme (3.7) with the Lipschitz constant \(L<\frac{\Gamma (\alpha +1)}{2T^{\alpha }}\). We consider the following iteration process:

$$\begin{aligned} \begin{aligned} U^m(x)=&\,\displaystyle \frac{T^{\alpha }}{4^{\alpha }\Gamma (\alpha )}\mathcal {I}_{x,N}\left[ (x+1)^{\alpha }\displaystyle \int _{-1}^1\big (1-\theta \big )^{\alpha -1} \mathcal {I}_{\theta ,N}^{\alpha -1,0}F\Big (\xi (x,\theta ),U^{m-1}\big (\xi (x,\theta )\big )\Big )d\theta \right] \\&-\displaystyle \frac{T^{\alpha }(x+1)}{2^{\alpha +1}\Gamma (\alpha )}\int _{-1}^1\big (1-\lambda \big )^{\alpha -1}\mathcal {I}_{\lambda ,N}^{\alpha -1,0}F(\lambda ,U^{m-1}(\lambda ))d\lambda . \end{aligned}\end{aligned}$$
(5.5)

Let \(\widetilde{U}^m(x)=U^m(x)-U^{m-1}(x).\) Then we have from (5.5) and (4.8) that

$$\begin{aligned} \begin{aligned} \widetilde{U}^m(x)&=\displaystyle \frac{T^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\mathcal {I}_{x,N}\left[ \displaystyle \int _{-1}^{x}\big (x-\xi \big )^{\alpha -1} {}_{x}\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\Big (F\big (\xi ,U^{m-1}(\xi )\big )-F\big (\xi ,U^{m-2}(\xi )\big )\Big )d\xi \right] \\&\quad -\displaystyle \frac{T^{\alpha }(x+1)}{2^{\alpha +1}\Gamma (\alpha )}\int _{-1}^1\big (1-\lambda \big )^{\alpha -1}\mathcal {I}_{\lambda ,N}^{\alpha -1,0}\Big (F(\lambda ,U^{m-1}(\lambda ))-F(\lambda ,U^{m-2}(\lambda ))\Big )d\lambda \\&=:A_1+A_2, \end{aligned}\end{aligned}$$
(5.6)

where

$$\begin{aligned} A_1= & {} \displaystyle \frac{T^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\mathcal {I}_{x,N}\left[ \displaystyle \int _{-1}^{x}\big (x-\xi \big )^{\alpha -1} {}_{x}\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\Big (F\big (\xi ,U^{m-1}(\xi )\big )-F\big (\xi ,U^{m-2}(\xi )\big )\Big )d\xi \right] ,\\ A_2= & {} -\displaystyle \frac{T^{\alpha }(x+1)}{2^{\alpha +1}\Gamma (\alpha )}\int _{-1}^1\big (1-\lambda \big )^{\alpha -1}\mathcal {I}_{\lambda ,N}^{\alpha -1,0}\Big (F(\lambda ,U^{m-1}(\lambda ))-F(\lambda ,U^{m-2}(\lambda ))\Big )d\lambda .\end{aligned}$$

By (3.3), (4.4) and the Cauchy–Schwarz inequality, we derive that

$$\begin{aligned}\begin{aligned}&\Vert A_1\Vert \\&\quad = \displaystyle \frac{T^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\Big \Vert \mathcal {I}_{x,N}\displaystyle \int _{-1}^x(x-\xi )^{\alpha -1} {}_x\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\big (F(\xi ,U^{m-1}(\xi ))-F(\xi ,U^{m-2}(\xi ))\big )d\xi \Big \Vert \\ {}&\quad = \displaystyle \frac{T^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\left[ \displaystyle \sum _{j=0}^N\omega _j\left( \displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1} {}_{x_j}\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\big (F(\xi ,U^{m-1}(\xi ))-F(\xi ,U^{m-2}(\xi ))\big )d\xi \right) ^2\right] ^{\frac{1}{2}}\\&\quad \le \displaystyle \frac{T^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\left[ \displaystyle \sum _{j=0}^N\omega _j\displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1}d\xi \displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1}\Big |{}_{x_j}\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\big (F(\xi ,U^{m-1}(\xi ))\right. \\&\left. \qquad -F(\xi ,U^{m-2}(\xi ))\big )\Big |^2d\xi \right] ^{\frac{1}{2}} \\ {}&\quad = \displaystyle \frac{T^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\left[ \displaystyle \sum _{j=0}^N\omega _j\displaystyle \frac{(x_j+1)^{2\alpha }}{2^\alpha \alpha } \displaystyle \sum _{k=0}^{N}\Big |F(\xi _k^{\alpha -1,0},U^{m-1}(\xi _k^{\alpha -1,0}))\right. \\&\left. \qquad -F(\xi _k^{\alpha -1,0},U^{m-2}(\xi _k^{\alpha -1,0}))\Big |^2 \omega _k^{\alpha -1,0}\right] ^{\frac{1}{2}}. \end{aligned}\end{aligned}$$

Therefore, by (4.4), (4.13) and the Lipschitz condition, we further deduce that for \(L<\frac{\Gamma (\alpha +1)}{2T^{\alpha }},\)

$$\begin{aligned} \Vert A_1\Vert&\le \displaystyle \frac{LT^{\alpha }}{2^{\alpha }\Gamma (\alpha )}\left[ \displaystyle \sum _{j=0}^N\omega _j\displaystyle \frac{(x_j+1)^{2\alpha }}{2^\alpha \alpha } \displaystyle \sum _{k=0}^{N}\Big |U^{m-1}(\xi _k^{\alpha -1,0})-U^{m-2}(\xi _k^{\alpha -1,0})\Big |^2\omega _k^{\alpha -1,0}\right] ^{\frac{1}{2}}\nonumber \\&\le \displaystyle \frac{\alpha }{2^{\alpha +1}}\left[ \displaystyle \sum _{j=0}^N\omega _j\displaystyle \frac{(x_j+1)^{\alpha }}{\alpha }\displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1}\Big |{}_{x_j}\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\widetilde{U}^{m-1}(\xi )\Big |^2d\xi \right] ^{\frac{1}{2}}\nonumber \\&\le \displaystyle \frac{\alpha }{2^{\alpha +1}}\left( \displaystyle \sum _{j=0}^N\omega _j\displaystyle \frac{(x_j+1)^{\alpha }}{\alpha }\right) ^{\frac{1}{2}}\max _{0 \le j \le N} \left( \displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1}\Big |{}_{x_j}\widetilde{\mathcal {I}}_{\xi ,N}^{\alpha -1,0}\widetilde{U}^{m-1}(\xi )\Big |^2d\xi \right) ^{\frac{1}{2}}\nonumber \\&= \displaystyle \frac{\alpha }{2^{\alpha +1}}\left( \displaystyle \sum _{j=0}^N\omega _j\displaystyle \frac{(x_j+1)^{\alpha }}{\alpha }\right) ^{\frac{1}{2}}\max _{0 \le j \le N} \left( \displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1}\Big |\widetilde{U}^{m-1}(\xi )\Big |^2d\xi \right) ^{\frac{1}{2}}\nonumber \\&\le \displaystyle \frac{\alpha }{2^{\alpha +1}}\sqrt{\displaystyle \frac{8}{3\alpha }}\max _{0 \le j \le N} \left( \displaystyle \int _{-1}^{x_j}(x_j-\xi )^{\alpha -1}\Big |\widetilde{U}^{m-1}(\xi )\Big |^2d\xi \right) ^{\frac{1}{2}}\nonumber \\&\le \displaystyle \frac{\alpha }{2^{\alpha +1}}\sqrt{\displaystyle \frac{2^{\alpha +2}}{3\alpha }}\left( \displaystyle \int _{-1}^1\Big |\widetilde{U}^{m-1}(\xi )\Big |^2d\xi \right) ^{\frac{1}{2}}\nonumber \\&= \sqrt{\displaystyle \frac{\alpha }{3\times 2^\alpha }}\Vert \widetilde{U}^{m-1}\Vert \nonumber . \end{aligned}$$

We next estimate the term \(\Vert A_2\Vert \). By the Cauchy–Schwarz inequality, we know that

$$\begin{aligned}\begin{aligned} \Vert A_2\Vert&=\displaystyle \frac{T^{\alpha }\Vert x+1\Vert }{2^{\alpha +1}\Gamma (\alpha )} \Big |\displaystyle \int _{-1}^1\big (1-\lambda \big )^{\alpha -1}\mathcal {I}_{\lambda ,N}^{\alpha -1,0} \Big (F(\lambda ,U^{m-1}(\lambda ))-F(\lambda ,U^{m-2}(\lambda ))\Big )d\lambda \Big |\\&\le \displaystyle \frac{T^{\alpha }}{2^{\alpha +1}\Gamma (\alpha )}\sqrt{\displaystyle \frac{2^{\alpha +3}}{3\alpha }} \Big [\displaystyle \int _{-1}^1\big (1-\lambda \big )^{\alpha -1}\Big |\mathcal {I}_{\lambda ,N}^{\alpha -1,0} \Big (F(\lambda ,U^{m-1}(\lambda ))-F(\lambda ,U^{m-2}(\lambda ))\Big )\Big |^2d\lambda \Big ]^{\frac{1}{2}}. \end{aligned}\end{aligned}$$

The previous result, along with (3.3) and the Lipschitz condition, yields

$$\begin{aligned} \Vert A_2\Vert&\le \displaystyle \frac{T^{\alpha }}{2^{\alpha +1}\Gamma (\alpha )}\sqrt{\displaystyle \frac{2^{\alpha +3}}{3\alpha }} \left( \displaystyle \sum _{j=0}^{N}\omega _j^{\alpha -1,0}\Big |F\left( x_j^{\alpha -1,0},U^{m-1}(x_j^{\alpha -1,0})\right) \right. \nonumber \\&\left. \quad -F\left( x_j^{\alpha -1,0},U^{m-2}(x_j^{\alpha -1,0})\right) \Big |^2\right) ^{\frac{1}{2}}\nonumber \\&\le \displaystyle \frac{LT^{\alpha }}{2^{\alpha +1}\Gamma (\alpha )}\sqrt{\displaystyle \frac{2^{\alpha +3}}{3\alpha }} \left( \displaystyle \sum _{j=0}^{N}\omega _j^{\alpha -1,0}\Big |\widetilde{U}^{m-1}(x_j^{\alpha -1,0})\Big |^2\right) ^{\frac{1}{2}}\nonumber \\&= \displaystyle \frac{LT^{\alpha }}{2^{\alpha +1}\Gamma (\alpha )}\sqrt{\displaystyle \frac{2^{\alpha +3}}{3\alpha }} \left( \displaystyle \int ^1_{-1}\big (1-\lambda \big )^{\alpha -1}\Big |\widetilde{U}^{m-1}(\lambda )\Big |^2d\lambda \right) ^{\frac{1}{2}}\nonumber \\&\le \sqrt{\displaystyle \frac{\alpha }{12}} \Vert \widetilde{U}^{m-1}\Vert ,\qquad \forall \alpha \in (1,2). \end{aligned}$$
(5.7)

Hence

$$\begin{aligned} \Vert \widetilde{U}^m\Vert \le \left( \sqrt{\displaystyle \frac{\alpha }{3\times 2^\alpha }}+\sqrt{\displaystyle \frac{\alpha }{12}}\right) \Vert \widetilde{U}^{m-1}\Vert . \end{aligned}$$

Since

$$\begin{aligned} \sqrt{\displaystyle \frac{\alpha }{3\times 2^\alpha }}+\sqrt{\displaystyle \frac{\alpha }{12}}<1,\qquad \forall \alpha \in (1,2), \end{aligned}$$

we have \(\Vert \widetilde{U}^m\Vert \rightarrow 0\) as \(m\rightarrow \infty .\) This implies the existence of solution of (3.7). It is easy to prove the uniqueness of solution of (3.7).

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Wang, C., Wang, Z. & Wang, L. A Spectral Collocation Method for Nonlinear Fractional Boundary Value Problems with a Caputo Derivative. J Sci Comput 76, 166–188 (2018). https://doi.org/10.1007/s10915-017-0616-3

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