{"title":"非线性Schrödinger方程的可积与不可积离散模型的数值比较研究","authors":"","doi":"10.23952/jnfa.2023.29","DOIUrl":null,"url":null,"abstract":". In this paper, we study efficiency of numerical simulation for integrable and nonintegrable discrete Non-linear Schr ¨ odinger equations (NLSE). We first discretize the NLSE into two classical spatial models, nonintegrable direct discrete model and integrable Ablowitz-Ladik model. By some simple transformations and Doubarx transformation, we obtain two integrable models from Ablowitz-Ladik model. Then, five different kinds of schemes can be applied to simulate four models in bright and dark cases for comparing the performance in preserving the conserved quantities’ approximations of NLSE. The numerical experiments indicate that Gauss symplectic method is more efficient than nonsymplectic schemes and splitting schemes when simulating the same model. Both intergrable models and nonintergrable model have their own advantages in preserving the conserved quanti-ties’ approximations. For the three integrable models, Ablowitz-Ladik Model and the model which has a general symplectic structure have similar simulation effects, and the model owing a cononical symplectic structure has low efficiency because the complicated Doubarx transformations make the model difficult to solve. Moreover, symplectic scheme and symmetric scheme have overwhelming superiorities over nonsymplectic schemes in preserving the invariants of Hamiltonian system.","PeriodicalId":44514,"journal":{"name":"Journal of Nonlinear Functional Analysis","volume":"2018 1","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative numerical study of integrable and nonintegrable discrete models of nonlinear Schrödinger equations\",\"authors\":\"\",\"doi\":\"10.23952/jnfa.2023.29\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". In this paper, we study efficiency of numerical simulation for integrable and nonintegrable discrete Non-linear Schr ¨ odinger equations (NLSE). We first discretize the NLSE into two classical spatial models, nonintegrable direct discrete model and integrable Ablowitz-Ladik model. By some simple transformations and Doubarx transformation, we obtain two integrable models from Ablowitz-Ladik model. Then, five different kinds of schemes can be applied to simulate four models in bright and dark cases for comparing the performance in preserving the conserved quantities’ approximations of NLSE. The numerical experiments indicate that Gauss symplectic method is more efficient than nonsymplectic schemes and splitting schemes when simulating the same model. Both intergrable models and nonintergrable model have their own advantages in preserving the conserved quanti-ties’ approximations. For the three integrable models, Ablowitz-Ladik Model and the model which has a general symplectic structure have similar simulation effects, and the model owing a cononical symplectic structure has low efficiency because the complicated Doubarx transformations make the model difficult to solve. Moreover, symplectic scheme and symmetric scheme have overwhelming superiorities over nonsymplectic schemes in preserving the invariants of Hamiltonian system.\",\"PeriodicalId\":44514,\"journal\":{\"name\":\"Journal of Nonlinear Functional Analysis\",\"volume\":\"2018 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nonlinear Functional Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23952/jnfa.2023.29\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nonlinear Functional Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23952/jnfa.2023.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
Comparative numerical study of integrable and nonintegrable discrete models of nonlinear Schrödinger equations
. In this paper, we study efficiency of numerical simulation for integrable and nonintegrable discrete Non-linear Schr ¨ odinger equations (NLSE). We first discretize the NLSE into two classical spatial models, nonintegrable direct discrete model and integrable Ablowitz-Ladik model. By some simple transformations and Doubarx transformation, we obtain two integrable models from Ablowitz-Ladik model. Then, five different kinds of schemes can be applied to simulate four models in bright and dark cases for comparing the performance in preserving the conserved quantities’ approximations of NLSE. The numerical experiments indicate that Gauss symplectic method is more efficient than nonsymplectic schemes and splitting schemes when simulating the same model. Both intergrable models and nonintergrable model have their own advantages in preserving the conserved quanti-ties’ approximations. For the three integrable models, Ablowitz-Ladik Model and the model which has a general symplectic structure have similar simulation effects, and the model owing a cononical symplectic structure has low efficiency because the complicated Doubarx transformations make the model difficult to solve. Moreover, symplectic scheme and symmetric scheme have overwhelming superiorities over nonsymplectic schemes in preserving the invariants of Hamiltonian system.
期刊介绍:
Journal of Nonlinear Functional Analysis focuses on important developments in nonlinear functional analysis and its applications with a particular emphasis on topics include, but are not limited to: Approximation theory; Asymptotic behavior; Banach space geometric constant and its applications; Complementarity problems; Control theory; Dynamic systems; Fixed point theory and methods of computing fixed points; Fluid dynamics; Functional differential equations; Iteration theory, iterative and composite equations; Mathematical biology and ecology; Miscellaneous applications of nonlinear analysis; Multilinear algebra and tensor computation; Nonlinear eigenvalue problems and nonlinear spectral theory; Nonsmooth analysis, variational analysis, convex analysis and their applications; Numerical analysis; Optimal control; Optimization theory; Ordinary differential equations; Partial differential equations; Positive operator inequality and its applications in operator equation spectrum theory and so forth; Semidefinite programming polynomial optimization; Variational and other types of inequalities involving nonlinear mappings; Variational inequalities.