Direct application of Padé approximant for solving nonlinear differential equations
- Hector Vazquez-Leal^{1}Email author,
- Brahim Benhammouda^{2},
- Uriel Filobello-Nino^{1},
- Arturo Sarmiento-Reyes^{3},
- Victor Manuel Jimenez-Fernandez^{1},
- Jose Luis Garcia-Gervacio^{4},
- Jesus Huerta-Chua^{5},
- Luis Javier Morales-Mendoza^{6} and
- Mario Gonzalez-Lee^{6}
https://doi.org/10.1186/2193-1801-3-563
© Vazquez-Leal et al.; licensee Springer. 2014
Received: 23 June 2014
Accepted: 10 September 2014
Published: 27 September 2014
Abstract
This work presents a direct procedure to apply Padé method to find approximate solutions for nonlinear differential equations. Moreover, we present some cases study showing the strength of the method to generate highly accurate rational approximate solutions compared to other semi-analytical methods. The type of tested nonlinear equations are: a highly nonlinear boundary value problem, a differential-algebraic oscillator problem, and an asymptotic problem. The high accurate handy approximations obtained by the direct application of Padé method shows the high potential if the proposed scheme to approximate a wide variety of problems. What is more, the direct application of the Padé approximant aids to avoid the previous application of an approximative method like Taylor series method, homotopy perturbation method, Adomian Decomposition method, homotopy analysis method, variational iteration method, among others, as tools to obtain a power series solutions to post-treat with the Padé approximant.
AMS Subject Classification
34L30
Keywords
1 Introduction
Solving differential equations is an important issue in sciences because many physical phenomena are modelled using such equations. The Padé method is a well established resummation method from literature. It can increase the domain of convergence of truncate power series (Bararnia et al. 2012; Guerrero et al. 2013; Torabi and Yaghoobi 2011; Vazquez-Leal and Guerrero 2013). It is has been applied to the improve the accuracy of truncated power obtained by power series method (PSM) (Forsyth 1906; Geddes 1979; Ince 1956; Vazquez-Leal and Guerrero 2013), Adomian Decomposition method (ADM) (Wazwaz 2006; Wang et al. 2011), homotopy perturbation method (HPM) (Bararnia et al. 2012; Rashidi and Keimanesh 2010; Torabi and Yaghoobi 2011), homotopy analysis method (HAM) (Guerrero et al. 2013), differential transform method (DTM) (Rashidi and Keimanesh 2010; Rashidi et al. 2010; Rashidi and Pour 2010a, 2010b), among others, during the solution procedure for linear and nonlinear differential equations. Nonetheless, in this work, we propose that the solution of a differential equation can be directly expressed as a rational power series of the independent variable, in other words as a Padé approximant. The proposed procedure will be described by solving several nonlinear problems and comparing results with other semi-analytic methods. The direct application of Padé eradicates the necessity to obtain a power series solution (by some approximative method) to post-treat it with the Padé approximant. Instead, we substitute a Padé approximant of a given order directly to the nonlinear differential equation; it results a residual power series in terms of the independent variable. Next, from the lowest order, we equate each coefficient of such power series to zero, resulting a system of nonlinear algebraic equations (NAEs). Finally, we resolve the NAEs in order to minimize the residual error of the differential equation.
This paper is organized as follows. In Section 2, we introduce the basic concepts of the Padé approximant. Next, the procedure to approximate nonlinear differential equations with Padé is presented in Section 3. In Section 4 some cases study are presented. In Section 5, numerical simulations and a discussion about the results are provided. Finally, a brief conclusion is given in Section 6.
2 Padé approximant
where we considered q_{0} = 1, and the numerator and denominator have no common factors.
From (5), we calculate first all the coefficients q_{ i },1 ≤ i ≤ M. Then, we determine the coefficient p_{ i },0 ≤ i ≤ L from (6).
Note that for a fixed value of L + M + 1, the error (3) is smallest when the numerator and denominator of (2) have the same degree or when the numerator has degree one higher than the denominator.
3 Padé applied to solve nonlinear differential equations
where L_{1} and N, are a linear and a non-linear operator, respectively; B is a boundary operator, Γ is the boundary of domain Ω, and ∂ u/∂ η denotes differentiation along the normal drawn outwards from Ω.
where v_{0},v_{1},… and w_{0},w_{1},… are unknowns to be determined by the Padé method, L, M are the order of the numerator and denominator, and x_{0} is an arbitrary constant.
- 1.
The boundary conditions of (7) are substituted in (9) to generate an equation for each boundary condition. It is important to notice, that there is an algebraic equation for each boundary condition, hence, the rest of equations required to generate a NAEs (with the same number of variables and equations) are obtained from the next step.
- 2.
u(x) from (9) is substituted into (7), then, we regroup the resulting equation in terms of the x-powers. It is important to notice that the operators L _{1} and N will be applied to u(x). After this, the regrouping procedure will include the eradication of the denominator terms emanated from the Padé approximant (9). In this way, the resulting expression is a power series that represents the residual error of the differential equation (7).
- 3.
In order to reduce the residual error; from the lowest order, we equate each coefficient of the x-powers in the resulting residual power series to zero to obtain an algebraic equation in terms of the unknown coefficients of (9).
- 4.
Aforementioned steps generates a NAEs in terms of the unknowns from (9).
- 5.
Finally, we solve the NAEs to obtain v _{0},v _{1},… and w _{0},w _{1},….
4 Cases study
In this section, we will solve several nonlinear problems of different types to show the validity and power of the direct application of Padé method to solve a broad spectrum of equations.
4.1 A boundary value problem
where prime denotes differentiation with respect to x and n is known as Troesch’s parameter.
where conditions are obtained by using variable transformation (11).
where w_{0} = 1, x_{0} = 0, and L = M = 8.
corresponding to u (0) = 0 and $u\left(1\right)=\text{tanh}\left(\frac{n}{4}\right)$, respectively.
for n = 0.5 and n = 1, respectively.
where (18) or (19) are used depending on the value of n.
4.2 Differential-algebraic equation
where w_{1,0} and w_{2,0} are considered as 1 to simplify the process of solution, and t_{0} = 0.
corresponding to $y\left(0\right)=\frac{\sqrt{2}}{2}$ and $z\left(0\right)=\frac{\sqrt{2}}{2}$, respectively.
Coefficients from Padé approximant ( 27) for DAEs ( 21)
i | v _{1,i} | w _{1,i} | v _{2,i} | w _{2,i} |
---|---|---|---|---|
0 | 0.7071067812 | 1 | 0.7071067812 | 1 |
1 | 0.6944478949 | -0.01790236873 | -0.710785391 | -0.005202339869 |
2 | -0.3552705766 | 0.015473901 | -0.3381563291 | 0.01657239326 |
3 | -0.1007688245 | -0.0002670569686 | 0.1079008196 | -0.0001006409133 |
4 | 0.02599872528 | 0.0001213625776 | 0.02316102487 | 0.0001405811897 |
5 | 0.003720336719 | -1.958219599e-06 | -0.004157250847 | -9.478457134e-07 |
6 | -0.0006440541915 | 6.292610474e-07 | -0.0005234980593 | 7.978499480e-07 |
7 | -5.257637767e-05 | -9.004870369e-09 | 6.112387339e-05 | -5.582330983e-09 |
8 | 6.857858371e-06 | 2.327029094e-09 | 4.879283240e-06 | 3.277470106e-09 |
9 | 3.140311387e-07 | -2.662291749e-11 | -3.785438873e-07 | -2.120465882e-11 |
10 | -3.286935291e-08 | 5.983862645e-12 | -1.897258525e-08 | 9.536760323e-12 |
11 | -6.790217810e-10 | -4.175830173e-14 | 8.457510263e-10 | -4.313868912e-14 |
12 | 5.933734260e-11 | 8.658872139e-15 | 2.337878489e-11 | 1.599248188e-14 |
4.3 Asymptotic problem
where w_{0} = 1, t_{0} = 0, and L = M = 4.
Next, using (31) and (34) to obtain the coefficients from Padé expression (30), and substituting t by expansion point (t-1.7), results
5 Numerical simulation and discussion
On one side, semi-analytic methods like: generalized homotopy method (GHM) (Vazquez-Leal 2013), homotopy perturbation method (Araghi and Rezapour 2011; Araghi and Sotoodeh 2012; Bayat et al. 2013, 2014; Biazar and Eslami 2011; Biazar and Ghanbari 2012; Filobello-Nino et al. 2012a, 2012b; He 1999, 2009; Khan et al. 2012a, 2012b; Vazquez-Leal 2012; Vazquez-Leal et al. 2012a, 2012b, 2012d), homotopy analysis method (Hassana and El-Tawil 2011; He 2004; Rashidi et al2012a,2012b; Tan and Abbasbandy2008), variational iteration method (Abbasbandy2007; Chang 2010; Khan et al. 2012c), among others (Khan et al. 2012d), need an initial approximation for the sought solutions and the calculus of one or several adjustment parameters. If the initial approximation is properly chosen, the results can be highly accurate, nonetheless, there is not a general method to choose such initial approximation. This issue motivates the use of adjustment parameters obtained by minimizing the least-squares error with respect to the numerical solution. On the other side, the Padé method obtain its coefficients using a straightforward procedure. Furthermore, at least for low-order approximations, the solution can be easily obtained using the “solve” or “fsolve” commands of MAPLE or equivalent routines from Mathematica or MATLAB.
x | Exact | This work | HPM | ADM | HPM | HPM | HAM | LDTM |
---|---|---|---|---|---|---|---|---|
(20) | (Vazquez-Leal et al.2012c) | (Deeba et al.2000) | (Feng et al.2007) | (Mirmoradia et al.2009) | (Hassana and El-Tawil2011) | (Khuri2003) | ||
0.1 | 0.0959443493 | 0.0959443493 | 0.0959443155 | 0.0959383534 | 0.0959395656 | 0.095948026 | 0.0959446190 | 0.0959443520 |
0.2 | 0.1921287477 | 0.1921287477 | 0.1921286848 | 0.1921180592 | 0.1921193244 | 0.192135797 | 0.1921292845 | 0.1921287539 |
0.3 | 0.2887944009 | 0.2887944009 | 0.2887943176 | 0.2887803297 | 0.2887806940 | 0.288804238 | 0.2887952148 | 0.2887944107 |
0.4 | 0.3861848464 | 0.3861848464 | 0.3861847539 | 0.3861687095 | 0.3861675428 | 0.386196642 | 0.3861859313 | 0.3861848612 |
0.5 | 0.4845471647 | 0.4845471647 | 0.4845470753 | 0.4845302901 | 0.4845274183 | 0.4845599 | 0.4845485110 | 0.4845471832 |
0.6 | 0.5841332484 | 0.5841332484 | 0.5841331729 | 0.5841169798 | 0.5841127822 | 0.584145785 | 0.5841348222 | 0.5841332650 |
0.7 | 0.6852011483 | 0.6852011483 | 0.6852010943 | 0.6851868451 | 0.6851822495 | 0.685212297 | 0.6852028604 | 0.6852011675 |
0.8 | 0.7880165227 | 0.7880165227 | 0.7880164925 | 0.7880055691 | 0.7880018367 | 0.788025104 | 0.7880181729 | 0.7880165463 |
0.9 | 0.8928542161 | 0.8928542161 | 0.8928542059 | 0.8928480234 | 0.8928462193 | 0.892859085 | 0.8928553997 | 0.8928542363 |
Order | [12/12] | 2 | 6 | 2 | 2 | 6 | 3 | |
A.A.R.E. | 0 | 1.83327e(-07) | 3.47802e(-05) | 3.57932e(-05) | 2.44418e(-05) | 2.51374e(-06) | 3.10957e(-08) |
x | Exact | This work | HPM | ADM | HPM | HPM | HAM | LDTM |
---|---|---|---|---|---|---|---|---|
(Erdogan and Ozis2011) | (20) | (Vazquez-Leal et al.2012c) | (Deeba et al.2000) | (Feng et al.2007) | (Mirmoradia et al.2009) | (Hassana and El-Tawil2011) | (Khuri2003) | |
0.1 | 0.0846612565 | 0.0846612565 | 0.08466075858 | 0.084248760 | 0.0843817004 | 0.084934415 | 0.0846732692 | 0.08466308972 |
0.2 | 0.1701713582 | 0.1701713582 | 0.1701704581 | 0.169430700 | 0.1696207644 | 0.170697546 | 0.1701954538 | 0.1701750442 |
0.3 | 0.2573939080 | 0.2573939081 | 0.2573927827 | 0.256414500 | 0.2565929224 | 0.258133224 | 0.2574302342 | 0.2573994845 |
0.4 | 0.3472228551 | 0.3472228551 | 0.3472217324 | 0.346085720 | 0.3462107378 | 0.348116627 | 0.3472715981 | 0.3472303763 |
0.5 | 0.4405998351 | 0.4405998352 | 0.4405989511 | 0.439401985 | 0.4394422743 | 0.44157274 | 0.4406610140 | 0.4406093753 |
0.6 | 0.5385343980 | 0.5385343981 | 0.5385339413 | 0.537365700 | 0.5373300622 | 0.539498234 | 0.5386072529 | 0.5385460046 |
0.7 | 0.6421286091 | 0.6421286092 | 0.6421286573 | 0.641083800 | 0.6410104651 | 0.642987984 | 0.7526899495 | 0.6421421393 |
0.8 | 0.7526080939 | 0.7526080940 | 0.7526085475 | 0.751788000 | 0.7517335467 | 0.753267551 | 0.7526899495 | 0.7526226886 |
0.9 | 0.8713625196 | 0.8713625198 | 0.8713630450 | 0.870908700 | 0.8708835371 | 0.871733059 | 0.8714249118 | 0.8713748860 |
Order | [12/12] | 2 | 6 | 2 | 2 | 6 | 3 | |
A.A.R.E. | 1.46588e(-10) | 2.54568e(-06) | 0.002714577 | 0.002320107 | 0.002044737 | 0.019244326 | 2.05e(-05) |
Relative error (R.E.) of exact solution ( 22) versus Padé approximation ( 27)
t | Exacty(t) | Exactz(t) | R.E.y(t)of (27) | R.E.z(t)of (27) |
---|---|---|---|---|
-10 | -0.2086321515 | -0.9779941847 | 0.09330825406 | 0.09330825406 |
-9 | -0.9356781623 | -0.3528546112 | 0.002548752664 | 0.002548752664 |
-8 | -0.8024659858 | 0.5966978646 | 0.0002522953745 | 0.0002522953745 |
-7 | 0.0685297173 | 0.9976490755 | 0.0001597267828 | 0.0001597267828 |
-6 | 0.8765195143 | 0.4813663272 | 3.803747675e-07 | 3.803747675e-07 |
-5 | 0.8786413122 | -0.4774824024 | 5.388747443e-09 | 5.388747443e-09 |
-4 | 0.0729443397 | -0.9973360132 | 3.131783529e-10 | 3.131783529e-10 |
-3 | -0.7998173223 | -0.6002434930 | 2.587566848e-14 | 2.587566848e-14 |
-2 | -0.9372306267 | 0.3487101265 | 9.922514670e-19 | 9.922514670e-19 |
-1 | -0.2129584152 | 0.9770612639 | 1.391674918e-25 | 1.391674918e-25 |
0 | 0.7071067812 | 0.7071067812 | 0.0000000000 | 0.0000000000 |
1 | 0.9770612639 | -0.2129584152 | 2.900333665e-26 | 2.900333665e-26 |
2 | 0.3487101265 | -0.9372306267 | 2.436171789e-18 | 2.436171789e-18 |
3 | -0.6002434930 | -0.7998173223 | 3.003755589e-14 | 3.003755589e-14 |
4 | -0.9973360132 | 0.0729443397 | 1.897834931e-11 | 1.897834931e-11 |
5 | -0.4774824024 | 0.8786413122 | 7.783182416e-09 | 7.783182416e-09 |
6 | 0.4813663272 | 0.8765195143 | 5.122538684e-07 | 5.122538684e-07 |
7 | 0.9976490755 | 0.0685297173 | 7.591788287e-06 | 7.591788287e-06 |
8 | 0.5966978646 | -0.8024659858 | 0.0002175967642 | 0.0002175967642 |
9 | -0.3528546112 | -0.9356781623 | 0.003968289586 | 0.003968289586 |
10 | -0.9779941847 | -0.2086321515 | 0.01052925646 | 0.01052925646 |
The accuracy of approximations (33) and (35) for the quadratic Riccati problem (28) is depicted in Figure 1. Moreover, we have suggested a strategy to increase the domain of convergence of the Padé method by changing its expansion point. As depicted in Figure 1, the approximation (35) obtained by expanding at t = 1.7 is far more accurate than (33) obtained by expanding at t = 1.7. It is important to notice that the expansion point was arbitrary choose for this case study; therefore, further work is required to deduce a systematic algorithm to choose optimal expansion points. Furthermore, in (Abbasbandy 2006) was reported a power series solution for the same equation with poor convergence, making necessary to solve the problem by a multi-stage version of HPM method. The advantage of our solution, in this case, is that we do not need to use a complicated segmented method; therefore, this approach generates simpler solutions. In addition, in (Tsai and Chen 2010) was reported the combination of Laplace Adomian Decomposition Method with Padé (LADM-Padé) of order [13/12] to obtain a similar result to our [4/4] order Padé solution. Furthermore, in (Abbasbandy 2007) was reported a power series solutions with short domain of convergence. A HAM solution in terms of exponential expressions was reported in (Tan and Abbasbandy 2008), presenting a high accurate solution with a larger domain than the proposed solution; for this case, we can increase the order of the Padé approximation to obtain a good agreement with HAM solution. Moreover, in order to show the advantage of the proposed method, we calculated 250 terms of the power series solution using the well established series method (using the command dsolve of Maple 16), resulting a poor region of convergence, followed by (33). Finally, as depicted in Figure 1, the best domain of convergence was obtained from the Padé approximant (35) due to the expansion point change.
The direct application of the Padé approximant to obtain rational solutions of nonlinear differential equations circumvent the old requirement of using Taylor series method (Vazquez-Leal et al. 2014), HPM, VIM, HAM, DTM, PSM, ADM and others, as tools to obtain a power series solutions to post-process later by the application Padé approximant. Therefore, this new straightforward methodology reduce the computational effort producing good results.
In general terms, we know from literature (Bararnia et al. 2012; Guerrero et al.2013; Torabi and Yaghoobi 2011; Vazquez-Leal and Guerrero 2013) that larger values for M and L, can lead to better results for Padé approximant, this considering that we count in advance with a suitable power series (large enough) obtained using an extra approximative method as aforementioned. Then, our proposal has a strong advantage because we do not require a power series to post-process with Padé approximant, because the method consist in the direct application of Padé. However, a systematic procedure to obtain the optimal order [L/M] is still a pending issue to study in a future research derived from this paper. Finally, in the present study, we restricted the research to nonsingular initial conditions and Dirichlet finite interval boundary conditions; nonetheless, further work is required to deal with singular initial condition problems, Neumann boundary conditions, infinity boundary conditions, among others.
6 Conclusions
This work presented the direct application of Padé method as a technique with high potential to solve nonlinear differential equations. Also, a comparison between the results of applying the proposed procedure and other semi-analytical was shown. The results showed that Padé is a powerful method to solve different nonlinear equations like the ones for: boundary value problems, differential-algebraic problems, and asymptotic problems. The method provided better results than many of the most used methods like: HPM, ADM, HAM, DTM, VIM, PSM, among others. Finally, further research should be performed to solve other kind of problems as: nonlinear fractional/partial differential equations, Pantograph equations, among others.
Declarations
Acknowledgements
We gratefully acknowledge the financial support of the National Council for Science and Technology of Mexico (CONACyT) through grant CB-2010-01 #157024. The authors would like to thank Rogelio-Alejandro Callejas-Molina, and Roberto Ruiz-Gomez for their contribution to this project.
Authors’ Affiliations
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