American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Numerical methods for PDE models related to pricing and expected lifetime of an extraction project under uncertainty
  • DCDS-B Home
  • This Issue
  • Next Article
    Smoothing dynamics of the non-autonomous stochastic Fitzhugh-Nagumo system on $\mathbb{R}^N$ driven by multiplicative noises
August  2019, 24(8): 3475-3502. doi: 10.3934/dcdsb.2018253

A randomized Milstein method for stochastic differential equations with non-differentiable drift coefficients

Raphael Kruse and  Yue Wu

Technische Universität Berlin, Institut für Mathematik, Secr. MA 5-3, Straße des 17. Juni 136, DE-10623 Berlin, Germany

Received  September 2017 Revised  February 2018 Published  August 2019 Early access  August 2018

Fund Project: The authors are supported by the German Research Foundation through FOR 2402.

In this paper a drift-randomized Milstein method is introduced for the numerical solution of non-autonomous stochastic differential equations with non-differentiable drift coefficient functions. Compared to standard Milstein-type methods we obtain higher order convergence rates in the $ L^p(Ω) $ and almost sure sense. An important ingredient in the error analysis are randomized quadrature rules for Hölder continuous stochastic processes. By this we avoid the use of standard arguments based on the Itō-Taylor expansion which are typically applied in error estimates of the classical Milstein method but require additional smoothness of the drift and diffusion coefficient functions. We also discuss the optimality of our convergence rates. Finally, the question of implementation is addressed in a numerical experiment.

Keywords: Milstein method, stochastic differential equations, strong convergence, Monte Carlo methods, randomized quadrature rules.
Mathematics Subject Classification: Primary: 65C30; Secondary: 65C05, 65L20, 60H10.
Citation: Raphael Kruse, Yue Wu. A randomized Milstein method for stochastic differential equations with non-differentiable drift coefficients. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 3475-3502. doi: 10.3934/dcdsb.2018253
References:
[1]

W.-J. Beyn and R. Kruse, Two-sided error estimates for the stochastic theta method, Discrete Contin. Dyn. Syst. Ser. B, 14 (2010), 389-407, URL http://dx.doi.org/10.3934/dcdsb.2010.14.389. doi: 10.3934/dcdsb.2010.14.389.  Google Scholar

[2]

D. L. Burkholder, Martingale transforms, Ann. Math. Statist., 37 (1966), 1494-1504.  doi: 10.1214/aoms/1177699141.  Google Scholar

[3]

J. M. C. Clark and R. J. Cameron, The maximum rate of convergence of discrete approximations for stochastic differential equations, in Stochastic Differential Systems (Proc. IFIP-WG 7/1 Working Conf., Vilnius, 1978), vol. 25 of Lecture Notes in Control and Information Sci., Springer, Berlin, 1980,162-171.  Google Scholar

[4]

T. Daun, On the randomized solution of initial value problems, J. Complexity, 27 (2011), 300-311, URL http://dx.doi.org/10.1016/j.jco.2010.07.002. doi: 10.1016/j.jco.2010.07.002.  Google Scholar

[5]

E. Emmrich, Discrete versions of Gronwall's lemma and their application to the numerical analysis of parabolic problems, TU Berlin, FB Mathematik, Preprint. Google Scholar

[6]

J. G. Gaines and T. J. Lyons, Random generation of stochastic area integrals, SIAM J. Appl. Math., 54 (1994), 1132-1146, URL http://dx.doi.org/10.1137/S0036139992235706. doi: 10.1137/S0036139992235706.  Google Scholar

[7]

M. B. Giles and L. Szpruch, Antithetic multilevel Monte Carlo estimation for multidimensional SDEs without Lévy area simulation, Ann. Appl. Probab., 24 (2014), 1585-1620, URL http://dx.doi.org/10.1214/13-AAP957. doi: 10.1214/13-AAP957.  Google Scholar

[8]

I. Gyöngy, A note on Euler's approximations, Potential Anal. , 8 (1998), 205-216, URL http://dx.doi.org/10.1023/A:1008605221617. Google Scholar

[9]

S. Haber, A modified Monte-Carlo quadrature, Math. Comp., 20 (1966), 361-368.  doi: 10.1090/S0025-5718-1966-0210285-0.  Google Scholar

[10]

S. Haber, A modified Monte-Carlo quadrature. Ⅱ, Math. Comp., 21 (1967), 388-397.  doi: 10.1090/S0025-5718-1967-0234606-9.  Google Scholar

[11]

S. Heinrich and B. Milla, The randomized complexity of initial value problems, J. Complexity, 24 (2008), 77-88.  doi: 10.1016/j.jco.2007.09.002.  Google Scholar

[12]

M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients, Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci., 467 (2011), 1563-1576, URL http://dx.doi.org/10.1098/rspa.2010.0348. doi: 10.1098/rspa.2010.0348.  Google Scholar

[13]

A. Jentzen and A. Neuenkirch, A random Euler scheme for Carathéodory differential equations, J. Comput. Appl. Math., 224 (2009), 346-359, URL http://dx.doi.org/10.1016/j.cam.2008.05.060. doi: 10.1016/j.cam.2008.05.060.  Google Scholar

[14]

B. Kacewicz, Optimal solution of ordinary differential equations, J. Complexity, 3 (1987), 451-465, URL http://dx.doi.org/10.1016/0885-064X(87)90011-2. doi: 10.1016/0885-064X(87)90011-2.  Google Scholar

[15]

B. Kacewicz, Asymptotic setting (revisited): Analysis of a boundary-value problem and a relation to a classical approximation result, J. Complexity, 20 (2004), 796-806, URL https://doi.org/10.1016/j.jco.2003.08.006. doi: 10.1016/j.jco.2003.08.006.  Google Scholar

[16]

B. Kacewicz, Almost optimal solution of initial-value problems by randomized and quantum algorithms, J. Complexity, 22 (2006), 676-690, URL http://dx.doi.org/10.1016/j.jco.2006.03.001. doi: 10.1016/j.jco.2006.03.001.  Google Scholar

[17]

I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, vol. 113 of Graduate Texts in Mathematics, 2nd edition, Springer-Verlag, New York, 1991, URL https://doi.org/10.1007/978-1-4612-0949-2. doi: 10.1007/978-1-4612-0949-2.  Google Scholar

[18]

P. E. Kloeden and A. Neuenkirch, The pathwise convergence of approximation schemes for stochastic differential equations, LMS J. Comput. Math., 10 (2007), 235-253, URL http://dx.doi.org/10.1112/S1461157000001388. doi: 10.1112/S1461157000001388.  Google Scholar

[19]

P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, 3rd edition, Springer, Berlin, 1999. doi: 10.1007/978-3-662-12616-5.  Google Scholar

[20]

R. Kruse, Characterization of bistability for stochastic multistep methods, BIT, 52 (2012), 109-140, URL http://dx.doi.org/10.1007/s10543-011-0341-5. doi: 10.1007/s10543-011-0341-5.  Google Scholar

[21]

R. Kruse, Consistency and stability of a Milstein-Galerkin finite element scheme for semilinear SPDE, Stoch. Partial Differ. Equ. Anal. Comput., 2 (2014), 471-516, URL http://dx.doi.org/10.1007/s40072-014-0037-3. doi: 10.1007/s40072-014-0037-3.  Google Scholar

[22]

R. Kruse and Y. Wu, Error analysis of randomized Runge-Kutta methods for differential equations with time-irregular coefficients, Comput. Methods Appl. Math., 17 (2017), 479-498.  doi: 10.1515/cmam-2016-0048.  Google Scholar

[23]

X. Mao, Stochastic Differential Equations and Applications, 2nd edition, Horwood Publishing Limited, Chichester, 2008, URL http://dx.doi.org/10.1533/9780857099402. doi: 10.1533/9780857099402.  Google Scholar

[24]

G. N. Milstein, Approximate integration of stochastic differential equations, Teor. Verojatnost. i Primenen., 19 (1974), 583-588, In Russian.  Google Scholar

[25]

G. N. Milstein, Approximate integration of stochastic differential equations, Theory Probab. Appl., 19 (1975), 557-562, Translated by K. Durr. Google Scholar

[26]

G. N. Milstein, Numerical Integration of Stochastic Differential Equations, vol. 313 of Mathematics and its Applications, Kluwer Academic Publishers Group, Dordrecht, 1995, Translated and revised from the 1988 Russian original. doi: 10.1007/978-94-015-8455-5.  Google Scholar

[27]

G. N. Milstein and M. V. Tretyakov, Stochastic Numerics for Mathematical Physics, Scientific Computation, Springer-Verlag, Berlin, 2004. doi: 10.1007/978-3-662-10063-9.  Google Scholar

[28]

P. M. Morkisz and P. Przyby lowicz, Optimal pointwise approximation of SDE's from inexact information, J. Comput. Appl. Math., 324 (2017), 85-100, URL http://dx.doi.org/10.1016/j.cam.2017.04.023. doi: 10.1016/j.cam.2017.04.023.  Google Scholar

[29]

P. Przyby lowicz, Minimal asymptotic error for one-point approximation of SDEs with timeirregular coefficients, J. Comput. Appl. Math., 282 (2015), 98-110, URL http://dx.doi.org/10.1016/j.cam.2015.01.003. doi: 10.1016/j.cam.2015.01.003.  Google Scholar

[30]

P. Przyby lowicz, Optimal global approximation of SDEs with time-irregular coefficients in asymptotic setting, Appl. Math. Comput., 270 (2015), 441-457, URL http://dx.doi.org/10.1016/j.amc.2015.08.055. doi: 10.1016/j.amc.2015.08.055.  Google Scholar

[31]

P. Przyby lowicz and P. Morkisz, Strong approximation of solutions of stochastic differential equations with time-irregular coefficients via randomized Euler algorithm, Appl. Numer. Math., 78 (2014), 80-94, URL http://dx.doi.org/10.1016/j.apnum.2013.12.003. doi: 10.1016/j.apnum.2013.12.003.  Google Scholar

[32]

T. Rydén and M. Wiktorsson, On the simulation of iterated Itô integrals, Stochastic Process. Appl., 91 (2001), 151-168.  doi: 10.1016/S0304-4149(00)00053-3.  Google Scholar

[33]

M. N. Spijker, Stability and Convergence of Finite-Difference Methods, vol. 1968 of Doctoral dissertation, University of Leiden, Rijksuniversiteit te Leiden, Leiden, 1968.  Google Scholar

[34]

M. N. Spijker, On the structure of error estimates for finite-difference methods, Numer. Math., 18 (1971/72), 73-100.  doi: 10.1007/BF01398460.  Google Scholar

[35]

G. Stengle, Numerical methods for systems with measurable coefficients, Appl. Math. Lett., 3 (1990), 25-29, URL http://dx.doi.org/10.1016/0893-9659(90)90040-I. doi: 10.1016/0893-9659(90)90040-I.  Google Scholar

[36]

G. Stengle, Error analysis of a randomized numerical method, Numer. Math. , 70 (1995), 119-128, URL http://dx.doi.org/10.1007/s002110050113. doi: 10.1007/s002110050113.  Google Scholar

[37]

F. Stummel, Approximation Methods in Analysis, Matematisk Institut, Aarhus Universitet, Aarhus, 1973, Lectures delivered during the spring term, 1973, Lecture Notes Series, No. 35.  Google Scholar

[38]

J. F. Traub, G. W. Wasilkowski and H. Woźniakowski, Information-based Complexity, Computer Science and Scientific Computing, Academic Press, Inc., Boston, MA, 1988, With contributions by A. G. Werschulz and T. Boult.  Google Scholar

[39]

M. Wiktorsson, Joint characteristic function and simultaneous simulation of iterated Itô integrals for multiple independent Brownian motions, Ann. Appl. Probab., 11 (2001), 470-487.  doi: 10.1214/aoap/1015345301.  Google Scholar

show all references

References:
[1]

W.-J. Beyn and R. Kruse, Two-sided error estimates for the stochastic theta method, Discrete Contin. Dyn. Syst. Ser. B, 14 (2010), 389-407, URL http://dx.doi.org/10.3934/dcdsb.2010.14.389. doi: 10.3934/dcdsb.2010.14.389.  Google Scholar

[2]

D. L. Burkholder, Martingale transforms, Ann. Math. Statist., 37 (1966), 1494-1504.  doi: 10.1214/aoms/1177699141.  Google Scholar

[3]

J. M. C. Clark and R. J. Cameron, The maximum rate of convergence of discrete approximations for stochastic differential equations, in Stochastic Differential Systems (Proc. IFIP-WG 7/1 Working Conf., Vilnius, 1978), vol. 25 of Lecture Notes in Control and Information Sci., Springer, Berlin, 1980,162-171.  Google Scholar

[4]

T. Daun, On the randomized solution of initial value problems, J. Complexity, 27 (2011), 300-311, URL http://dx.doi.org/10.1016/j.jco.2010.07.002. doi: 10.1016/j.jco.2010.07.002.  Google Scholar

[5]

E. Emmrich, Discrete versions of Gronwall's lemma and their application to the numerical analysis of parabolic problems, TU Berlin, FB Mathematik, Preprint. Google Scholar

[6]

J. G. Gaines and T. J. Lyons, Random generation of stochastic area integrals, SIAM J. Appl. Math., 54 (1994), 1132-1146, URL http://dx.doi.org/10.1137/S0036139992235706. doi: 10.1137/S0036139992235706.  Google Scholar

[7]

M. B. Giles and L. Szpruch, Antithetic multilevel Monte Carlo estimation for multidimensional SDEs without Lévy area simulation, Ann. Appl. Probab., 24 (2014), 1585-1620, URL http://dx.doi.org/10.1214/13-AAP957. doi: 10.1214/13-AAP957.  Google Scholar

[8]

I. Gyöngy, A note on Euler's approximations, Potential Anal. , 8 (1998), 205-216, URL http://dx.doi.org/10.1023/A:1008605221617. Google Scholar

[9]

S. Haber, A modified Monte-Carlo quadrature, Math. Comp., 20 (1966), 361-368.  doi: 10.1090/S0025-5718-1966-0210285-0.  Google Scholar

[10]

S. Haber, A modified Monte-Carlo quadrature. Ⅱ, Math. Comp., 21 (1967), 388-397.  doi: 10.1090/S0025-5718-1967-0234606-9.  Google Scholar

[11]

S. Heinrich and B. Milla, The randomized complexity of initial value problems, J. Complexity, 24 (2008), 77-88.  doi: 10.1016/j.jco.2007.09.002.  Google Scholar

[12]

M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients, Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci., 467 (2011), 1563-1576, URL http://dx.doi.org/10.1098/rspa.2010.0348. doi: 10.1098/rspa.2010.0348.  Google Scholar

[13]

A. Jentzen and A. Neuenkirch, A random Euler scheme for Carathéodory differential equations, J. Comput. Appl. Math., 224 (2009), 346-359, URL http://dx.doi.org/10.1016/j.cam.2008.05.060. doi: 10.1016/j.cam.2008.05.060.  Google Scholar

[14]

B. Kacewicz, Optimal solution of ordinary differential equations, J. Complexity, 3 (1987), 451-465, URL http://dx.doi.org/10.1016/0885-064X(87)90011-2. doi: 10.1016/0885-064X(87)90011-2.  Google Scholar

[15]

B. Kacewicz, Asymptotic setting (revisited): Analysis of a boundary-value problem and a relation to a classical approximation result, J. Complexity, 20 (2004), 796-806, URL https://doi.org/10.1016/j.jco.2003.08.006. doi: 10.1016/j.jco.2003.08.006.  Google Scholar

[16]

B. Kacewicz, Almost optimal solution of initial-value problems by randomized and quantum algorithms, J. Complexity, 22 (2006), 676-690, URL http://dx.doi.org/10.1016/j.jco.2006.03.001. doi: 10.1016/j.jco.2006.03.001.  Google Scholar

[17]

I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, vol. 113 of Graduate Texts in Mathematics, 2nd edition, Springer-Verlag, New York, 1991, URL https://doi.org/10.1007/978-1-4612-0949-2. doi: 10.1007/978-1-4612-0949-2.  Google Scholar

[18]

P. E. Kloeden and A. Neuenkirch, The pathwise convergence of approximation schemes for stochastic differential equations, LMS J. Comput. Math., 10 (2007), 235-253, URL http://dx.doi.org/10.1112/S1461157000001388. doi: 10.1112/S1461157000001388.  Google Scholar

[19]

P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, 3rd edition, Springer, Berlin, 1999. doi: 10.1007/978-3-662-12616-5.  Google Scholar

[20]

R. Kruse, Characterization of bistability for stochastic multistep methods, BIT, 52 (2012), 109-140, URL http://dx.doi.org/10.1007/s10543-011-0341-5. doi: 10.1007/s10543-011-0341-5.  Google Scholar

[21]

R. Kruse, Consistency and stability of a Milstein-Galerkin finite element scheme for semilinear SPDE, Stoch. Partial Differ. Equ. Anal. Comput., 2 (2014), 471-516, URL http://dx.doi.org/10.1007/s40072-014-0037-3. doi: 10.1007/s40072-014-0037-3.  Google Scholar

[22]

R. Kruse and Y. Wu, Error analysis of randomized Runge-Kutta methods for differential equations with time-irregular coefficients, Comput. Methods Appl. Math., 17 (2017), 479-498.  doi: 10.1515/cmam-2016-0048.  Google Scholar

[23]

X. Mao, Stochastic Differential Equations and Applications, 2nd edition, Horwood Publishing Limited, Chichester, 2008, URL http://dx.doi.org/10.1533/9780857099402. doi: 10.1533/9780857099402.  Google Scholar

[24]

G. N. Milstein, Approximate integration of stochastic differential equations, Teor. Verojatnost. i Primenen., 19 (1974), 583-588, In Russian.  Google Scholar

[25]

G. N. Milstein, Approximate integration of stochastic differential equations, Theory Probab. Appl., 19 (1975), 557-562, Translated by K. Durr. Google Scholar

[26]

G. N. Milstein, Numerical Integration of Stochastic Differential Equations, vol. 313 of Mathematics and its Applications, Kluwer Academic Publishers Group, Dordrecht, 1995, Translated and revised from the 1988 Russian original. doi: 10.1007/978-94-015-8455-5.  Google Scholar

[27]

G. N. Milstein and M. V. Tretyakov, Stochastic Numerics for Mathematical Physics, Scientific Computation, Springer-Verlag, Berlin, 2004. doi: 10.1007/978-3-662-10063-9.  Google Scholar

[28]

P. M. Morkisz and P. Przyby lowicz, Optimal pointwise approximation of SDE's from inexact information, J. Comput. Appl. Math., 324 (2017), 85-100, URL http://dx.doi.org/10.1016/j.cam.2017.04.023. doi: 10.1016/j.cam.2017.04.023.  Google Scholar

[29]

P. Przyby lowicz, Minimal asymptotic error for one-point approximation of SDEs with timeirregular coefficients, J. Comput. Appl. Math., 282 (2015), 98-110, URL http://dx.doi.org/10.1016/j.cam.2015.01.003. doi: 10.1016/j.cam.2015.01.003.  Google Scholar

[30]

P. Przyby lowicz, Optimal global approximation of SDEs with time-irregular coefficients in asymptotic setting, Appl. Math. Comput., 270 (2015), 441-457, URL http://dx.doi.org/10.1016/j.amc.2015.08.055. doi: 10.1016/j.amc.2015.08.055.  Google Scholar

[31]

P. Przyby lowicz and P. Morkisz, Strong approximation of solutions of stochastic differential equations with time-irregular coefficients via randomized Euler algorithm, Appl. Numer. Math., 78 (2014), 80-94, URL http://dx.doi.org/10.1016/j.apnum.2013.12.003. doi: 10.1016/j.apnum.2013.12.003.  Google Scholar

[32]

T. Rydén and M. Wiktorsson, On the simulation of iterated Itô integrals, Stochastic Process. Appl., 91 (2001), 151-168.  doi: 10.1016/S0304-4149(00)00053-3.  Google Scholar

[33]

M. N. Spijker, Stability and Convergence of Finite-Difference Methods, vol. 1968 of Doctoral dissertation, University of Leiden, Rijksuniversiteit te Leiden, Leiden, 1968.  Google Scholar

[34]

M. N. Spijker, On the structure of error estimates for finite-difference methods, Numer. Math., 18 (1971/72), 73-100.  doi: 10.1007/BF01398460.  Google Scholar

[35]

G. Stengle, Numerical methods for systems with measurable coefficients, Appl. Math. Lett., 3 (1990), 25-29, URL http://dx.doi.org/10.1016/0893-9659(90)90040-I. doi: 10.1016/0893-9659(90)90040-I.  Google Scholar

[36]

G. Stengle, Error analysis of a randomized numerical method, Numer. Math. , 70 (1995), 119-128, URL http://dx.doi.org/10.1007/s002110050113. doi: 10.1007/s002110050113.  Google Scholar

[37]

F. Stummel, Approximation Methods in Analysis, Matematisk Institut, Aarhus Universitet, Aarhus, 1973, Lectures delivered during the spring term, 1973, Lecture Notes Series, No. 35.  Google Scholar

[38]

J. F. Traub, G. W. Wasilkowski and H. Woźniakowski, Information-based Complexity, Computer Science and Scientific Computing, Academic Press, Inc., Boston, MA, 1988, With contributions by A. G. Werschulz and T. Boult.  Google Scholar

[39]

M. Wiktorsson, Joint characteristic function and simultaneous simulation of iterated Itô integrals for multiple independent Brownian motions, Ann. Appl. Probab., 11 (2001), 470-487.  doi: 10.1214/aoap/1015345301.  Google Scholar

Figure 1.  Numerical experiment for SDE (48): Step sizes versus $L^2$ errors
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Numerical experiment for SDE (48): CPU time versus $L^2$ errors
Figure Options

Download full-size image

Download as PowerPoint slide

Table Listing 1.  A sample implementation of (9) in Python
Table Options

Download as excel

[1]

Michael B. Giles, Kristian Debrabant, Andreas Rössler. Analysis of multilevel Monte Carlo path simulation using the Milstein discretisation. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 3881-3903. doi: 10.3934/dcdsb.2018335
[2]

Giacomo Dimarco. The moment guided Monte Carlo method for the Boltzmann equation. Kinetic & Related Models, 2013, 6 (2) : 291-315. doi: 10.3934/krm.2013.6.291
[3]

Weijun Zhan, Qian Guo, Yuhao Cong. The truncated Milstein method for super-linear stochastic differential equations with Markovian switching. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021201
[4]

Junhao Hu, Chenggui Yuan. Strong convergence of neutral stochastic functional differential equations with two time-scales. Discrete & Continuous Dynamical Systems - B, 2019, 24 (11) : 5831-5848. doi: 10.3934/dcdsb.2019108
[5]

Leyu Hu, Xingju Cai. Convergence of a randomized Douglas-Rachford method for linear system. Numerical Algebra, Control & Optimization, 2020, 10 (4) : 463-474. doi: 10.3934/naco.2020045
[6]

Yulan Lu, Minghui Song, Mingzhu Liu. Convergence rate and stability of the split-step theta method for stochastic differential equations with piecewise continuous arguments. Discrete & Continuous Dynamical Systems - B, 2019, 24 (2) : 695-717. doi: 10.3934/dcdsb.2018203
[7]

Guillaume Bal, Ian Langmore, Youssef Marzouk. Bayesian inverse problems with Monte Carlo forward models. Inverse Problems & Imaging, 2013, 7 (1) : 81-105. doi: 10.3934/ipi.2013.7.81
[8]

Ajay Jasra, Kody J. H. Law, Yaxian Xu. Markov chain simulation for multilevel Monte Carlo. Foundations of Data Science, 2021, 3 (1) : 27-47. doi: 10.3934/fods.2021004
[9]

Theodore Papamarkou, Alexey Lindo, Eric B. Ford. Geometric adaptive Monte Carlo in random environment. Foundations of Data Science, 2021, 3 (2) : 201-224. doi: 10.3934/fods.2021014
[10]

Imtiaz Ahmad, Siraj-ul-Islam, Mehnaz, Sakhi Zaman. Local meshless differential quadrature collocation method for time-fractional PDEs. Discrete & Continuous Dynamical Systems - S, 2020, 13 (10) : 2641-2654. doi: 10.3934/dcdss.2020223
[11]

Can Huang, Zhimin Zhang. The spectral collocation method for stochastic differential equations. Discrete & Continuous Dynamical Systems - B, 2013, 18 (3) : 667-679. doi: 10.3934/dcdsb.2013.18.667
[12]

Bahareh Akhtari, Esmail Babolian, Andreas Neuenkirch. An Euler scheme for stochastic delay differential equations on unbounded domains: Pathwise convergence. Discrete & Continuous Dynamical Systems - B, 2015, 20 (1) : 23-38. doi: 10.3934/dcdsb.2015.20.23
[13]

Jiakou Wang, Margaret J. Slattery, Meghan Henty Hoskins, Shile Liang, Cheng Dong, Qiang Du. Monte carlo simulation of heterotypic cell aggregation in nonlinear shear flow. Mathematical Biosciences & Engineering, 2006, 3 (4) : 683-696. doi: 10.3934/mbe.2006.3.683
[14]

Sanjay Khattri. Another note on some quadrature based three-step iterative methods for non-linear equations. Numerical Algebra, Control & Optimization, 2013, 3 (3) : 549-555. doi: 10.3934/naco.2013.3.549
[15]

Julian Koellermeier, Roman Pascal Schaerer, Manuel Torrilhon. A framework for hyperbolic approximation of kinetic equations using quadrature-based projection methods. Kinetic & Related Models, 2014, 7 (3) : 531-549. doi: 10.3934/krm.2014.7.531
[16]

Ying Liu, Yabing Sun, Weidong Zhao. Explicit multistep stochastic characteristic approximation methods for forward backward stochastic differential equations. Discrete & Continuous Dynamical Systems - S, 2021  doi: 10.3934/dcdss.2021044
[17]

Weiyin Fei, Liangjian Hu, Xuerong Mao, Dengfeng Xia. Advances in the truncated Euler–Maruyama method for stochastic differential delay equations. Communications on Pure & Applied Analysis, 2020, 19 (4) : 2081-2100. doi: 10.3934/cpaa.2020092
[18]

Christopher Rackauckas, Qing Nie. Adaptive methods for stochastic differential equations via natural embeddings and rejection sampling with memory. Discrete & Continuous Dynamical Systems - B, 2017, 22 (7) : 2731-2761. doi: 10.3934/dcdsb.2017133
[19]

Quan Zhou, Yabing Sun. High order one-step methods for backward stochastic differential equations via Itô-Taylor expansion. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021233
[20]

Weijun Zhou, Youhua Zhou. On the strong convergence of a modified Hestenes-Stiefel method for nonconvex optimization. Journal of Industrial & Management Optimization, 2013, 9 (4) : 893-899. doi: 10.3934/jimo.2013.9.893

2020 Impact Factor: 1.327

Tools

Metrics

  • PDF downloads (268)
  • HTML views (821)
  • Cited by (1)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences