{"id":"https://openalex.org/W2575581645","doi":null,"title":"Self-Calibration via Linear Least Squares","display_name":"Self-Calibration via Linear Least Squares","publication_year":2016,"publication_date":"2016-11-13","ids":{"openalex":"https://openalex.org/W2575581645","mag":"2575581645"},"language":"en","primary_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt1t71583v","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/1t71583v","pdf_url":"https://escholarship.org/uc/item/1t71583v","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://escholarship.org/uc/item/1t71583v","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047770219","display_name":"Shuyang Ling","orcid":"https://orcid.org/0000-0002-1326-3913","observed_orcids":["https://orcid.org/0000-0002-1326-3913"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuyang Ling","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1326-3913","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080565728","display_name":"Thomas Strohmer","orcid":"https://orcid.org/0000-0003-2029-3317","observed_orcids":["https://orcid.org/0000-0003-2029-3317"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Strohmer","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2029-3317","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.4616,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.92059052,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9872999787330627,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9872999787330627,"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied 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/T11205","display_name":"Numerical methods in inverse problems","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/self-calibration","display_name":"self-calibration","score":0.996999979019165},{"id":"https://openalex.org/keywords/linear-least-squares","display_name":"linear least squares","score":0.8999999761581421},{"id":"https://openalex.org/keywords/blind-deconvolution","display_name":"blind deconvolution","score":0.996999979019165},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"sample complexity","score":0.6579999923706055},{"id":"https://openalex.org/keywords/stability-analysis","display_name":"stability analysis","score":0.7200000286102295},{"id":"https://openalex.org/keywords/real-time-algorithms","display_name":"real-time algorithms","score":0.6029999852180481},{"id":"https://openalex.org/keywords/imaging","display_name":"imaging","score":0.4440000057220459}],"concepts":[{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.5674757957458496},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5611448287963867},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5161129236221313},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.4571646451950073},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4571336507797241},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.45153385400772095},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.4246253967285156},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3916447162628174},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35890474915504456},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3325969874858856},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.32704293727874756},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.11101359128952026},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1098392903804779},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0811406672000885},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:escholarship.org/ark:/13030/qt1t71583v","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/1t71583v","pdf_url":"https://escholarship.org/uc/item/1t71583v","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},{"id":"mag:2575581645","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1611.04196","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:qt1t71583v","doi":null,"is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/1t71583v","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ling, Shuyang; & Strohmer, Thomas. (2016). Self-Calibration via Linear Least Squares. UC Davis: Department of Mathematics. Retrieved from: http://www.escholarship.org/uc/item/1t71583v","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt1t71583v","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/1t71583v","pdf_url":"https://escholarship.org/uc/item/1t71583v","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3230223053","display_name":"Harmonic analysis, non-convex optimization, and large data sets","funder_award_id":"1620455","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3824835158","display_name":"Methods and Algorithms from Harmonic Analysis for Threat Detection","funder_award_id":"1322393","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2575581645.pdf","grobid_xml":"https://content.openalex.org/works/W2575581645.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W57250413","https://openalex.org/W143004564","https://openalex.org/W584962943","https://openalex.org/W1489405158","https://openalex.org/W1497904071","https://openalex.org/W1506342804","https://openalex.org/W1526135953","https://openalex.org/W1546108826","https://openalex.org/W1953819449","https://openalex.org/W1972822190","https://openalex.org/W1981481674","https://openalex.org/W1983992752","https://openalex.org/W1998920622","https://openalex.org/W2041507040","https://openalex.org/W2046164006","https://openalex.org/W2057370874","https://openalex.org/W2069811854","https://openalex.org/W2102019642","https://openalex.org/W2102662752","https://openalex.org/W2115245044","https://openalex.org/W2131172946","https://openalex.org/W2140867429","https://openalex.org/W2142849059","https://openalex.org/W2233245434","https://openalex.org/W2233545183","https://openalex.org/W2248294424","https://openalex.org/W2268674159","https://openalex.org/W2280704529","https://openalex.org/W2302736609","https://openalex.org/W2323456480","https://openalex.org/W2355939603","https://openalex.org/W2516015953","https://openalex.org/W2518555133","https://openalex.org/W2531405675","https://openalex.org/W2541913904","https://openalex.org/W2950332050","https://openalex.org/W2962818938","https://openalex.org/W2963731874","https://openalex.org/W2963927955","https://openalex.org/W3098045837","https://openalex.org/W3104684837"],"related_works":["https://openalex.org/W2619514009","https://openalex.org/W3104684837","https://openalex.org/W2397031703","https://openalex.org/W2964212675","https://openalex.org/W2950998125","https://openalex.org/W1560310918","https://openalex.org/W2959475433","https://openalex.org/W2057370874","https://openalex.org/W2595307470","https://openalex.org/W2069811854"],"abstract_inverted_index":{"Whenever":[0],"we":[1,64,122],"use":[2],"devices":[3,51],"to":[4,16,34,58,95,180,198],"take":[5],"measurements,":[6,23],"calibration":[7,14,60,98],"is":[8,15,56,83,94,176],"indispensable.":[9],"While\\n":[10],"the":[11,22,44,54,66,79,97,102,105,109,150,171,200],"purpose":[12],"of":[13,46,119,173,204],"reduce":[17],"bias":[18],"and":[19,30,85,108,165,188,193,202],"uncertainty":[20,103],"in":[21,104,185],"it":[24],"can":[25,137],"be\\n":[26],"quite":[27],"difficult,":[28],"expensive":[29],"sometimes":[31],"even":[32],"impossible":[33],"implement.":[35],"We":[36],"study":[37],"a\\n":[38,148],"challenging":[39],"problem":[40],"called":[41],"self-calibration,":[42],"i.e.,":[43],"task":[45],"designing":[47],"an":[48],"algorithm":[49,55],"for\\n":[50,159],"so":[52],"that":[53],"able":[57],"perform":[59],"automatically.":[61],"More":[62],"precisely,\\n":[63],"consider":[65],"setup":[67],"$\\\\boldsymbol{y}":[68],"=":[69],"\\\\mathcal{A}(\\\\boldsymbol{d})":[70],"\\\\boldsymbol{x}":[71],"+\\n":[72],"\\\\boldsymbol{\\\\epsilon}$":[73],"where":[74,86],"only":[75],"partial":[76],"information":[77],"about":[78],"sensing":[80,106],"matrix\\n":[81],"$\\\\mathcal{A}(\\\\boldsymbol{d})$":[82,87],"known":[84],"linearly\\n":[88],"depends":[89],"on":[90],"$\\\\boldsymbol{d}$.":[91],"The":[92],"goal":[93],"estimate":[96],"parameter\\n":[99],"$\\\\boldsymbol{d}$":[100],"(resolve":[101],"process)":[107],"signal/object":[110],"of\\n":[111],"interests":[112],"$\\\\boldsymbol{x}$":[113],"simultaneously.":[114],"For":[115],"three":[116],"different":[117],"models":[118],"practical\\n":[120],"relevance":[121],"show":[123],"how":[124],"such":[125],"a":[126,141,181],"bilinear":[127],"inverse":[128],"problem,":[129],"including":[130],"blind":[131],"deconvolution":[132],"as":[133],"an\\n":[134],"important":[135],"example,":[136],"be":[138],"solved":[139],"via":[140],"simple":[142],"linear":[143],"least":[144],"squares":[145],"approach.":[146,206],"As":[147],"consequence,":[149],"proposed":[151],"algorithms":[152],"are":[153,168,191],"numerically":[154],"extremely":[155],"efficient,":[156],"thus":[157],"allowing":[158],"real-time":[160],"deployment.":[161],"Explicit":[162],"theoretical":[163],"guarantees":[164],"stability":[166],"theory":[167],"derived":[169],"and\\n":[170],"number":[172],"sampling":[174],"complexity":[175],"nearly":[177],"optimal":[178],"(up":[179],"poly-log":[182],"factor).":[183],"Applications\\n":[184],"imaging":[186],"sciences":[187],"signal":[189],"processing":[190],"discussed":[192],"numerical":[194],"simulations":[195],"are\\n":[196],"presented":[197],"demonstrate":[199],"effectiveness":[201],"efficiency":[203],"our":[205]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":6}],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2017-01-26T00:00:00"}