Naranmandula Bao, Junbiao Lu, Ruobing Cai, Yueheng Lan
{"title":"通过生成函数计算随机矩阵乘积的增长率","authors":"Naranmandula Bao, Junbiao Lu, Ruobing Cai, Yueheng Lan","doi":"10.1007/s43673-022-00057-0","DOIUrl":null,"url":null,"abstract":"<div><p>Random matrix products arise in many science and engineering problems. An efficient evaluation of its growth rate is of great interest to researchers in diverse fields. In the current paper, we reformulate this problem with a generating function approach, based on which two analytic methods are proposed to compute the growth rate. The new formalism is demonstrated in a series of examples including an Ising model subject to on-site random magnetic fields, which seems very efficient and easy to implement. Through an extensive comparison with numerical computation, we see that the analytic results are valid in a region of considerable size.The formulation could be conveniently applied to stochastic processes with more complex structures.</p></div>","PeriodicalId":100007,"journal":{"name":"AAPPS Bulletin","volume":"32 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43673-022-00057-0.pdf","citationCount":"0","resultStr":"{\"title\":\"Computing growth rates of random matrix products via generating functions\",\"authors\":\"Naranmandula Bao, Junbiao Lu, Ruobing Cai, Yueheng Lan\",\"doi\":\"10.1007/s43673-022-00057-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Random matrix products arise in many science and engineering problems. An efficient evaluation of its growth rate is of great interest to researchers in diverse fields. In the current paper, we reformulate this problem with a generating function approach, based on which two analytic methods are proposed to compute the growth rate. The new formalism is demonstrated in a series of examples including an Ising model subject to on-site random magnetic fields, which seems very efficient and easy to implement. Through an extensive comparison with numerical computation, we see that the analytic results are valid in a region of considerable size.The formulation could be conveniently applied to stochastic processes with more complex structures.</p></div>\",\"PeriodicalId\":100007,\"journal\":{\"name\":\"AAPPS Bulletin\",\"volume\":\"32 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s43673-022-00057-0.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AAPPS Bulletin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s43673-022-00057-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AAPPS Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43673-022-00057-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computing growth rates of random matrix products via generating functions
Random matrix products arise in many science and engineering problems. An efficient evaluation of its growth rate is of great interest to researchers in diverse fields. In the current paper, we reformulate this problem with a generating function approach, based on which two analytic methods are proposed to compute the growth rate. The new formalism is demonstrated in a series of examples including an Ising model subject to on-site random magnetic fields, which seems very efficient and easy to implement. Through an extensive comparison with numerical computation, we see that the analytic results are valid in a region of considerable size.The formulation could be conveniently applied to stochastic processes with more complex structures.