{"id":"https://openalex.org/W7151234872","doi":"https://doi.org/10.48550/arxiv.2604.04726","title":"A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Models","display_name":"A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Models","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151234872","doi":"https://doi.org/10.48550/arxiv.2604.04726"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04726","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04726","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.04726","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133101280","display_name":"Xiao Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133137915","display_name":"Shuang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9348999857902527,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9348999857902527,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.04399999976158142,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.0027000000700354576,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/coordinate-descent","display_name":"Coordinate descent","score":0.70660001039505},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.6251999735832214},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6222000122070312},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5595999956130981},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.5196999907493591},{"id":"https://openalex.org/keywords/multilinear-map","display_name":"Multilinear map","score":0.5015000104904175},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4851999878883362},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.47269999980926514},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.43459999561309814},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.3968000113964081}],"concepts":[{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.70660001039505},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6258000135421753},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.6251999735832214},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6222000122070312},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5889999866485596},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.5196999907493591},{"id":"https://openalex.org/C84392682","wikidata":"https://www.wikidata.org/wiki/Q1952404","display_name":"Multilinear map","level":2,"score":0.5015000104904175},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4851999878883362},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.47269999980926514},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.43459999561309814},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.38989999890327454},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C180623205","wikidata":"https://www.wikidata.org/wiki/Q1268589","display_name":"Outer product","level":3,"score":0.3547999858856201},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C41681595","wikidata":"https://www.wikidata.org/wiki/Q7917855","display_name":"Vectorization (mathematics)","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.3458000123500824},{"id":"https://openalex.org/C60321788","wikidata":"https://www.wikidata.org/wiki/Q1197190","display_name":"Multilinear algebra","level":5,"score":0.3237000107765198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32350000739097595},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.31929999589920044},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C6802819","wikidata":"https://www.wikidata.org/wiki/Q1072174","display_name":"Linear system","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C175694140","wikidata":"https://www.wikidata.org/wiki/Q980329","display_name":"Orthographic projection","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.25459998846054077},{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04726","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04726","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.04726","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04726","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Tensor-valued":[0],"data":[1],"arise":[2],"naturally":[3],"in":[4,155],"multidimensional":[5],"signal":[6],"and":[7,28,78,98,108,149,159,167],"imaging":[8],"problems,":[9],"such":[10,111],"as":[11],"biomedical":[12],"imaging.":[13],"When":[14],"incorporated":[15],"into":[16,124],"generalized":[17],"linear":[18],"models":[19],"(GLMs),":[20],"naive":[21],"vectorization":[22],"can":[23,94],"destroy":[24],"their":[25],"multi-way":[26],"structure":[27,50],"lead":[29],"to":[30],"high-dimensional,":[31],"ill-posed":[32],"estimation.":[33],"To":[34],"address":[35],"this":[36],"challenge,":[37],"Low":[38,66],"Separation":[39,67],"Rank":[40,68],"(LSR)":[41],"decompositions":[42],"reduce":[43],"model":[44],"complexity":[45],"by":[46,102,121],"imposing":[47],"low-rank":[48],"multilinear":[49],"on":[51],"the":[52,65,82,90,103,125,131,138,171],"coefficient":[53],"tensor.":[54],"A":[55],"representative":[56],"approach":[57],"for":[58,105],"estimating":[59],"LSR-based":[60],"tensor":[61],"GLMs":[62],"(LSR-TGLMs)":[63],"is":[64],"Tensor":[69],"Regression":[70],"(LSRTR)":[71],"algorithm,":[72],"which":[73,116],"adopts":[74],"block":[75,133],"coordinate":[76,134],"descent":[77],"enforces":[79],"orthogonality":[80],"of":[81],"factor":[83,140],"matrices":[84],"through":[85],"repeated":[86,91],"QR-based":[87],"projections.":[88],"However,":[89],"projection":[92],"steps":[93],"be":[95],"computationally":[96],"demanding":[97],"slow":[99],"convergence.":[100],"Motivated":[101],"need":[104],"scalable":[106],"estimation":[107,166],"classification":[109,184],"from":[110],"data,":[112],"we":[113],"propose":[114],"LSRTR-M,":[115],"incorporates":[117],"Muon":[118,143],"(MomentUm":[119],"Orthogonalized":[120],"Newton-Schulz)":[122],"updates":[123,141],"LSRTR":[126],"framework.":[127],"Specifically,":[128],"LSRTR-M":[129,152],"preserves":[130],"original":[132],"scheme":[135],"while":[136,162,181],"replacing":[137],"projection-based":[139],"with":[142],"steps.":[144],"Across":[145],"synthetic":[146],"linear,":[147],"logistic,":[148],"Poisson":[150],"LSR-TGLMs,":[151],"converges":[153],"faster":[154],"both":[156],"iteration":[157],"count":[158],"wall-clock":[160],"time,":[161],"achieving":[163],"lower":[164],"normalized":[165],"prediction":[168],"errors.":[169],"On":[170],"Vessel":[172],"MNIST":[173],"3D":[174],"task,":[175],"it":[176],"further":[177],"improves":[178],"computational":[179],"efficiency":[180],"maintaining":[182],"competitive":[183],"performance.":[185]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
