GRE methods for nonlinear model of evolution equation and limited ressource environment

Philippe Michel; Bhargav Kumar Kakumani

doi:10.3934/dcdsb.2019161

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December 2019, 24(12): 6653-6673. doi: 10.3934/dcdsb.2019161

GRE methods for nonlinear model of evolution equation and limited ressource environment

Philippe Michel ^1,, and Bhargav Kumar Kakumani ^2,

1.	Ecole Centrale de Lyon, University Claude Bernard Lyon 1, CNRS UMR 5208, Ecully 69130, France
2.	T.I.F.R. Centre for Applicable Mathematics, Bangalore 560065, India

^* Corresponding author: Philippe Michel

Received June 2018 Revised November 2018 Published December 2019 Early access July 2019

In this paper, we consider nonlocal nonlinear renewal equation (Markov chain, Ordinary differential equation and Partial Differential Equation). We show that the General Relative Entropy [29] can be extend to nonlinear problems and under some assumptions on the nonlinearity we prove the convergence of the solution to its steady state as time tends to infinity.

Keywords: Nonlinear renewal equation, steady state, convergence.

Mathematics Subject Classification: Primary: 92D25, 35A01, 35A02, 35B40.

Citation: Philippe Michel, Bhargav Kumar Kakumani. GRE methods for nonlinear model of evolution equation and limited ressource environment. Discrete & Continuous Dynamical Systems - B, 2019, 24 (12) : 6653-6673. doi: 10.3934/dcdsb.2019161

References:

[1]	B. Abdellaoui and T. M. Touaoula, Decay solution for the renewal equation with diffusion, Nonlinear Differ. Equ. Appl. (Nodea), 17 (2010), 271-288. doi: 10.1007/s00030-009-0053-6. Google Scholar
[2]	H. Behncke and S. Al-Nassir, On the Harvesting of Age Structured of Fish Populations, Communications in Mathematics and Applications, 8 (2017), 139-156. Google Scholar
[3]	P. Billingsley, Probability and Measure (3rd ed.), Wiley, New York, 1995. Google Scholar
[4]	J. W. Brewer, The age-dependent eigenfunctions of certain Kolmogorov equations of engineering, economics, and biology, Applied Mathematical Modeling, 13 (1989), 47-57. doi: 10.1016/0307-904X(89)90197-2. Google Scholar
[5]	V. Calvez, N. Lenuzza, D. Oelz, J. P. Deslys, P. Laurent, F. Mouthon and B. Perthame, Bimodality, prion aggregates infectivity and prediction of strain phenomenon, arXiv: preprint, 2008. Google Scholar
[6]	J. Clairambault, P. Michel and B. Perthame, A mathematical model of the cell cycle and its circadian control, Mathematical Modeling of Biological Systems, 1 (2006), 239-251. doi: 10.1007/978-0-8176-4558-8_21. Google Scholar
[7]	J. M. Cushing, An Introduction to Structured Population Dynamics, SIAM, Philadelphia, 1998. doi: 10.1137/1.9781611970005. Google Scholar
[8]	R. Dautray and J. Lions, Analyse Mathématique et Calcul Numérique Pour les Sciences Et les Techniques, Masson, Paris, 1987. Google Scholar
[9]	A. Devys, T. Goudon and P. Lafitte, A model describing the growth and the size distribution of multiple metastatic tumors, AIMS, 12 (2009), 731–767, Available from: http://hal.inria.fr/inria-00351489/fr/. doi: 10.3934/dcdsb.2009.12.731. Google Scholar
[10]	M. Doumic, B. Perthame and J. P. Zubelli, Numerical solution of an inverse problem in size-structured population dynamics, Inverse Problems, 25 (2009), 045008, 25 pp. doi: 10.1088/0266-5611/25/4/045008. Google Scholar
[11]	N. Echenim, Modelisation et Controle Multi-echelles du Processus de Selection des Follicules Ovulatoires, Phd Thesis, Universit Paris Sud-Ⅺ, 2006. Google Scholar
[12]	N. Echenim, D. Monniaux, M. Sorine and F. Clement, Multi-scale modeling of the follicle selection process in the ovary, Math. Biosci., 198 (2005), 57-79. doi: 10.1016/j.mbs.2005.05.003. Google Scholar
[13]	N. Echenim, F. Clément and M. Sorine, Multiscale modeling of follicular ovulation as a reachability problem, Multiscale Modeling and Simulation, 6 (2007), 895-912. doi: 10.1137/060664495. Google Scholar
[14]	K. -J. Engel and R. Nagel, A Short Course on Operator Semigroups, Universitext, Springer, 2006. Google Scholar
[15]	K.-J. Engel and R. Nagel, One-Parameter Semigroups for Linear Evolution Equations, Springer-Verlag, New York, 2000. Google Scholar
[16]	P. Gwiazda and B. Perthame, Invariants and exponential rate of convergence to steady state in the renewal equation, Markov Processes and Related Fields (MPRF), 12 (2006), 413-424. Google Scholar
[17]	M. Iannelli, Age-structured population. In encyclopedia of mathematics, Supplement Ⅱ. Hazewinkel M. (a cura di), Kluwer Academics, (2000), 21–23. Google Scholar
[18]	M. Iannelli, Mathematical theory of age-structured population dynamics, Applied Mathematics Monograph C.N.R., 7 (1995), In Pisa: Giardini editori e stampatori. Google Scholar
[19]	M. Iannelli and J. Ripoll, Two-sex age structured dynamics in a fixed sex-ratio population, Nonlinear Analysis: Real World Applications, 13 (2012), 2562-2577. doi: 10.1016/j.nonrwa.2012.03.002. Google Scholar
[20]	M. Iosifsecu, Finite Markov Processes and their Applications, John Wiley, New York, 1980. Google Scholar
[21]	B. K. Kakumani and S. K. Tumuluri, On a nonlinear renewal equation with diffusion, Math. Meth. Appl. Sci., 39 (2016), 697-708. doi: 10.1002/mma.3511. Google Scholar
[22]	B. K. Kakumani and S. K. Tumuluri, Extinction and blow-up phenomena in a non-linear gender structured population model, Nonlinear Analysis: Real World Applications, 28 (2016), 290-299. doi: 10.1016/j.nonrwa.2015.10.005. Google Scholar
[23]	M. G. Kreǐn and M. A. Rutman, Linear operators leaving invariant a cone in a Banach space, Amer. Math. Soc. Transl., (1950), 128 pp. Google Scholar
[24]	P. Laurencot and B. Perthame, Exponential decay for the growth-fragmentation/cell-division equation, Comm. Math. Sci., 7 (2009), 503-510. doi: 10.4310/CMS.2009.v7.n2.a12. Google Scholar
[25]	J. A. J. Metz and O. Diekmann, The Dynamics of Physiologically Structured Populations, Lecture Notes in Biomathematics, 68. Springer-Verlag, Berlin, 1986. doi: 10.1007/978-3-662-13159-6. Google Scholar
[26]	P. Michel, General relative entropy in a nonlinear McKendrick model, Stochastic Analysis and Partial Differential Equations, Contemp. Math., Amer. Math. Soc., Providence, RI, 429 (2007), 205–232. doi: 10.1090/conm/429/08238. Google Scholar
[27]	P. Michel, Optimal proliferation rate in a cell division model, Mathematical Modelling of Natural Phenomen, 1 (2006), 23-44. doi: 10.1051/mmnp:2008002. Google Scholar
[28]	P. Michel, Fitness optimization in a cell division model, Comptes Rendus Mathematique, 341 (2005), 731-736. doi: 10.1016/j.crma.2005.10.012. Google Scholar
[29]	P. Michel, S. Mischler and B. Perthame, General relative entropy inequality: An illustration on growth models., J. Math. Pures Appl., 84 (2005), 1235-1260. doi: 10.1016/j.matpur.2005.04.001. Google Scholar
[30]	P. Michel and T. M. Touaoula, Asymptotic behavior for a class of the renewal nonlinear equation with diffusion, Mathematical Methods in the Applied Sciences, 36 (2012), 323-335. doi: 10.1002/mma.2591. Google Scholar
[31]	S. Mischler, B. Perthame and L. Ryzhik, Stability in a nonlinear population maturation model, Mathematical Models and Methods in Applid Sciences, 12 (2002), 1751-1772. doi: 10.1142/S021820250200232X. Google Scholar
[32]	J. D. Murray, Mathematical Biology, I, An introduction, Third edition. Interdisciplinary Applied Mathematics, 17. Springer-Verlag, New York, 2002. Google Scholar
[33]	R. Nagel (ed.), One-Parameter Semigroups of Positive Operators, Lect. Notes in Math., Springer-Verlag, 1986. Google Scholar
[34]	B. Perthame, Transport Equations in Biology. Frontiers in Mathematics, Birkhauser Verlag, Basel, 2007. Google Scholar
[35]	B. Perthame, Mathematical tools for kinetic equations, Bull. Amer. Math. Soc. (N.S.), 41 (2004), 205–244 (electronic). doi: 10.1090/S0273-0979-04-01004-3. Google Scholar
[36]	B. Perthame, The general relative entropy principle applications in Perron-Frobenius and Floquet theories and a parabolic system for biomotors, Rend. Accad. Naz. Sci. XL Mem. Mat. Appl., 29 (2005), 307-325. Google Scholar
[37]	B. Perthame and S. K. Tumuluri, Nonlinear renewal equations, in: N. Bellomo, M. Chaplain, E. De Angelis (Eds.), Selected Topics on Cancer Modeling Genesis-Evolution-Immune Competition-Therapy, Modeling and Simulation in Science, Engineering and Technology, Birkhäuser, 2008, 65–96. Google Scholar
[38]	J. A. Silva and T. G. Hallam, Compensation and stability in nonlinear matrix models, Math Biosci., 110 (1992), 67-101. doi: 10.1016/0025-5564(92)90015-O. Google Scholar
[39]	H. R. Thieme, Mathematics in Population Biology, University Press, Princeton, NJ, 2003. Google Scholar
[40]	T. M. Touaoula and B. Abdellaoui, Decay solution for the renewal equation with diffusion, Nonlinear Differential Equations and Applications NoDEA, 17 (2010), 271-288. doi: 10.1007/s00030-009-0053-6. Google Scholar
[41]	S. K. Tumuluri, Steady state analysis of a non-linear renewal equation, Mathematical and Computer Modeling, 53 (2011), 1420-1435. doi: 10.1016/j.mcm.2010.02.050. Google Scholar
[42]	N. G. Van Kampen, Stochastic Processes in Physics and Chemistry, Lecture Notes in Math., 888, North-Holland Publishing Co., Amsterdam-New York, 1981. Google Scholar
[43]	G. F. Webb, Theory of Nonlinear Age-dependent Population Dynamics, Pure and Applied Mathematics, 89, Marcel Dekker, New York, 1985. Google Scholar
[44]	A. Wikan and O. Kristensen, Nonstationary and chaotic dynamics in age-structured population models, Discrete Dynamics in Nature and Society, 8 (2017), Art. ID 1964286, 11 pp. doi: 10.1155/2017/1964286. Google Scholar
[45]	K. Yosida, Functional Analysis (Classics in Mathematics), Springer-Verlag, Berlin, 1995. doi: 10.1007/978-3-642-61859-8. Google Scholar

show all references

References:

[1]	B. Abdellaoui and T. M. Touaoula, Decay solution for the renewal equation with diffusion, Nonlinear Differ. Equ. Appl. (Nodea), 17 (2010), 271-288. doi: 10.1007/s00030-009-0053-6. Google Scholar
[2]	H. Behncke and S. Al-Nassir, On the Harvesting of Age Structured of Fish Populations, Communications in Mathematics and Applications, 8 (2017), 139-156. Google Scholar
[3]	P. Billingsley, Probability and Measure (3rd ed.), Wiley, New York, 1995. Google Scholar
[4]	J. W. Brewer, The age-dependent eigenfunctions of certain Kolmogorov equations of engineering, economics, and biology, Applied Mathematical Modeling, 13 (1989), 47-57. doi: 10.1016/0307-904X(89)90197-2. Google Scholar
[5]	V. Calvez, N. Lenuzza, D. Oelz, J. P. Deslys, P. Laurent, F. Mouthon and B. Perthame, Bimodality, prion aggregates infectivity and prediction of strain phenomenon, arXiv: preprint, 2008. Google Scholar
[6]	J. Clairambault, P. Michel and B. Perthame, A mathematical model of the cell cycle and its circadian control, Mathematical Modeling of Biological Systems, 1 (2006), 239-251. doi: 10.1007/978-0-8176-4558-8_21. Google Scholar
[7]	J. M. Cushing, An Introduction to Structured Population Dynamics, SIAM, Philadelphia, 1998. doi: 10.1137/1.9781611970005. Google Scholar
[8]	R. Dautray and J. Lions, Analyse Mathématique et Calcul Numérique Pour les Sciences Et les Techniques, Masson, Paris, 1987. Google Scholar
[9]	A. Devys, T. Goudon and P. Lafitte, A model describing the growth and the size distribution of multiple metastatic tumors, AIMS, 12 (2009), 731–767, Available from: http://hal.inria.fr/inria-00351489/fr/. doi: 10.3934/dcdsb.2009.12.731. Google Scholar
[10]	M. Doumic, B. Perthame and J. P. Zubelli, Numerical solution of an inverse problem in size-structured population dynamics, Inverse Problems, 25 (2009), 045008, 25 pp. doi: 10.1088/0266-5611/25/4/045008. Google Scholar
[11]	N. Echenim, Modelisation et Controle Multi-echelles du Processus de Selection des Follicules Ovulatoires, Phd Thesis, Universit Paris Sud-Ⅺ, 2006. Google Scholar
[12]	N. Echenim, D. Monniaux, M. Sorine and F. Clement, Multi-scale modeling of the follicle selection process in the ovary, Math. Biosci., 198 (2005), 57-79. doi: 10.1016/j.mbs.2005.05.003. Google Scholar
[13]	N. Echenim, F. Clément and M. Sorine, Multiscale modeling of follicular ovulation as a reachability problem, Multiscale Modeling and Simulation, 6 (2007), 895-912. doi: 10.1137/060664495. Google Scholar
[14]	K. -J. Engel and R. Nagel, A Short Course on Operator Semigroups, Universitext, Springer, 2006. Google Scholar
[15]	K.-J. Engel and R. Nagel, One-Parameter Semigroups for Linear Evolution Equations, Springer-Verlag, New York, 2000. Google Scholar
[16]	P. Gwiazda and B. Perthame, Invariants and exponential rate of convergence to steady state in the renewal equation, Markov Processes and Related Fields (MPRF), 12 (2006), 413-424. Google Scholar
[17]	M. Iannelli, Age-structured population. In encyclopedia of mathematics, Supplement Ⅱ. Hazewinkel M. (a cura di), Kluwer Academics, (2000), 21–23. Google Scholar
[18]	M. Iannelli, Mathematical theory of age-structured population dynamics, Applied Mathematics Monograph C.N.R., 7 (1995), In Pisa: Giardini editori e stampatori. Google Scholar
[19]	M. Iannelli and J. Ripoll, Two-sex age structured dynamics in a fixed sex-ratio population, Nonlinear Analysis: Real World Applications, 13 (2012), 2562-2577. doi: 10.1016/j.nonrwa.2012.03.002. Google Scholar
[20]	M. Iosifsecu, Finite Markov Processes and their Applications, John Wiley, New York, 1980. Google Scholar
[21]	B. K. Kakumani and S. K. Tumuluri, On a nonlinear renewal equation with diffusion, Math. Meth. Appl. Sci., 39 (2016), 697-708. doi: 10.1002/mma.3511. Google Scholar
[22]	B. K. Kakumani and S. K. Tumuluri, Extinction and blow-up phenomena in a non-linear gender structured population model, Nonlinear Analysis: Real World Applications, 28 (2016), 290-299. doi: 10.1016/j.nonrwa.2015.10.005. Google Scholar
[23]	M. G. Kreǐn and M. A. Rutman, Linear operators leaving invariant a cone in a Banach space, Amer. Math. Soc. Transl., (1950), 128 pp. Google Scholar
[24]	P. Laurencot and B. Perthame, Exponential decay for the growth-fragmentation/cell-division equation, Comm. Math. Sci., 7 (2009), 503-510. doi: 10.4310/CMS.2009.v7.n2.a12. Google Scholar
[25]	J. A. J. Metz and O. Diekmann, The Dynamics of Physiologically Structured Populations, Lecture Notes in Biomathematics, 68. Springer-Verlag, Berlin, 1986. doi: 10.1007/978-3-662-13159-6. Google Scholar
[26]	P. Michel, General relative entropy in a nonlinear McKendrick model, Stochastic Analysis and Partial Differential Equations, Contemp. Math., Amer. Math. Soc., Providence, RI, 429 (2007), 205–232. doi: 10.1090/conm/429/08238. Google Scholar
[27]	P. Michel, Optimal proliferation rate in a cell division model, Mathematical Modelling of Natural Phenomen, 1 (2006), 23-44. doi: 10.1051/mmnp:2008002. Google Scholar
[28]	P. Michel, Fitness optimization in a cell division model, Comptes Rendus Mathematique, 341 (2005), 731-736. doi: 10.1016/j.crma.2005.10.012. Google Scholar
[29]	P. Michel, S. Mischler and B. Perthame, General relative entropy inequality: An illustration on growth models., J. Math. Pures Appl., 84 (2005), 1235-1260. doi: 10.1016/j.matpur.2005.04.001. Google Scholar
[30]	P. Michel and T. M. Touaoula, Asymptotic behavior for a class of the renewal nonlinear equation with diffusion, Mathematical Methods in the Applied Sciences, 36 (2012), 323-335. doi: 10.1002/mma.2591. Google Scholar
[31]	S. Mischler, B. Perthame and L. Ryzhik, Stability in a nonlinear population maturation model, Mathematical Models and Methods in Applid Sciences, 12 (2002), 1751-1772. doi: 10.1142/S021820250200232X. Google Scholar
[32]	J. D. Murray, Mathematical Biology, I, An introduction, Third edition. Interdisciplinary Applied Mathematics, 17. Springer-Verlag, New York, 2002. Google Scholar
[33]	R. Nagel (ed.), One-Parameter Semigroups of Positive Operators, Lect. Notes in Math., Springer-Verlag, 1986. Google Scholar
[34]	B. Perthame, Transport Equations in Biology. Frontiers in Mathematics, Birkhauser Verlag, Basel, 2007. Google Scholar
[35]	B. Perthame, Mathematical tools for kinetic equations, Bull. Amer. Math. Soc. (N.S.), 41 (2004), 205–244 (electronic). doi: 10.1090/S0273-0979-04-01004-3. Google Scholar
[36]	B. Perthame, The general relative entropy principle applications in Perron-Frobenius and Floquet theories and a parabolic system for biomotors, Rend. Accad. Naz. Sci. XL Mem. Mat. Appl., 29 (2005), 307-325. Google Scholar
[37]	B. Perthame and S. K. Tumuluri, Nonlinear renewal equations, in: N. Bellomo, M. Chaplain, E. De Angelis (Eds.), Selected Topics on Cancer Modeling Genesis-Evolution-Immune Competition-Therapy, Modeling and Simulation in Science, Engineering and Technology, Birkhäuser, 2008, 65–96. Google Scholar
[38]	J. A. Silva and T. G. Hallam, Compensation and stability in nonlinear matrix models, Math Biosci., 110 (1992), 67-101. doi: 10.1016/0025-5564(92)90015-O. Google Scholar
[39]	H. R. Thieme, Mathematics in Population Biology, University Press, Princeton, NJ, 2003. Google Scholar
[40]	T. M. Touaoula and B. Abdellaoui, Decay solution for the renewal equation with diffusion, Nonlinear Differential Equations and Applications NoDEA, 17 (2010), 271-288. doi: 10.1007/s00030-009-0053-6. Google Scholar
[41]	S. K. Tumuluri, Steady state analysis of a non-linear renewal equation, Mathematical and Computer Modeling, 53 (2011), 1420-1435. doi: 10.1016/j.mcm.2010.02.050. Google Scholar
[42]	N. G. Van Kampen, Stochastic Processes in Physics and Chemistry, Lecture Notes in Math., 888, North-Holland Publishing Co., Amsterdam-New York, 1981. Google Scholar
[43]	G. F. Webb, Theory of Nonlinear Age-dependent Population Dynamics, Pure and Applied Mathematics, 89, Marcel Dekker, New York, 1985. Google Scholar
[44]	A. Wikan and O. Kristensen, Nonstationary and chaotic dynamics in age-structured population models, Discrete Dynamics in Nature and Society, 8 (2017), Art. ID 1964286, 11 pp. doi: 10.1155/2017/1964286. Google Scholar
[45]	K. Yosida, Functional Analysis (Classics in Mathematics), Springer-Verlag, Berlin, 1995. doi: 10.1007/978-3-642-61859-8. Google Scholar

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GRE methods for nonlinear model of evolution equation and limited ressource environment

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GRE methods for nonlinear model of evolution equation and limited ressource environment

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