{"title":"Computing the nc-Rank via Discrete Convex Optimization on CAT(0) Spaces","authors":"Masaki Hamada, H. Hirai","doi":"10.1137/20m138836x","DOIUrl":null,"url":null,"abstract":"In this paper, we address the noncommutative rank (nc-rank) computation of a linear symbolic matrix A = A1x1 + A2x2 + · · ·+ Amxm, where each Ai is an n × n matrix over a field K, and xi (i = 1, 2, . . . ,m) are noncommutative variables. For this problem, polynomial time algorithms were given by Garg, Gurvits, Oliveira, and Wigderson for K = Q, and by Ivanyos, Qiao, and Subrahmanyam for an arbitrary field K. We present a significantly different polynomial time algorithm that works on an arbitrary field K. Our algorithm is based on a combination of submodular optimization on modular lattices and convex optimization on CAT(0) spaces.","PeriodicalId":48489,"journal":{"name":"SIAM Journal on Applied Algebra and Geometry","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2020-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Journal on Applied Algebra and Geometry","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1137/20m138836x","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 15
Abstract
In this paper, we address the noncommutative rank (nc-rank) computation of a linear symbolic matrix A = A1x1 + A2x2 + · · ·+ Amxm, where each Ai is an n × n matrix over a field K, and xi (i = 1, 2, . . . ,m) are noncommutative variables. For this problem, polynomial time algorithms were given by Garg, Gurvits, Oliveira, and Wigderson for K = Q, and by Ivanyos, Qiao, and Subrahmanyam for an arbitrary field K. We present a significantly different polynomial time algorithm that works on an arbitrary field K. Our algorithm is based on a combination of submodular optimization on modular lattices and convex optimization on CAT(0) spaces.