{"id":"https://openalex.org/W3002118509","doi":"https://doi.org/10.1109/tsp.2020.3037996","title":"Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery","display_name":"Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3002118509","doi":"https://doi.org/10.1109/tsp.2020.3037996","mag":"3002118509"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2020.3037996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.3037996","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2001.07181","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001341683","display_name":"Nan Meng","orcid":"https://orcid.org/0000-0001-7232-9348"},"institutions":[{"id":"https://openalex.org/I4210093666","display_name":"The Edgbaston Hospital","ror":"https://ror.org/007bv1h22","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210093666","https://openalex.org/I4210104184"]},{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nan Meng","raw_affiliation_strings":["School of Mathematics, University of Birmingham, Edgbaston, Birmingham, U.K"],"raw_orcid":"https://orcid.org/0000-0001-7232-9348","affiliations":[{"raw_affiliation_string":"School of Mathematics, University of Birmingham, Edgbaston, Birmingham, U.K","institution_ids":["https://openalex.org/I4210093666","https://openalex.org/I79619799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057733991","display_name":"Yun-Bin Zhao","orcid":"https://orcid.org/0000-0002-2388-9047"},"institutions":[{"id":"https://openalex.org/I4210099586","display_name":"Shenzhen Research Institute of Big Data","ror":"https://ror.org/00z1gwf89","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099586"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]},{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["CN","GB"],"is_corresponding":false,"raw_author_name":"Yun-Bin Zhao","raw_affiliation_strings":["Shenzhen Research Institute of Big Data, Chinese University of Hong Kong-Shenzhen, Shenzhen, Guangdong, China","University of Birmingham, Birmingham, U.K"],"raw_orcid":"https://orcid.org/0000-0002-2388-9047","affiliations":[{"raw_affiliation_string":"Shenzhen Research Institute of Big Data, Chinese University of Hong Kong-Shenzhen, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I4210099586","https://openalex.org/I4210116924"]},{"raw_affiliation_string":"University of Birmingham, Birmingham, U.K","institution_ids":["https://openalex.org/I79619799"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9654,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.95151657,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"68","issue":null,"first_page":"6594","last_page":"6606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.7493717670440674},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.7224382758140564},{"id":"https://openalex.org/keywords/restricted-isometry-property","display_name":"Restricted isometry property","score":0.7205647230148315},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7104323506355286},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5342739224433899},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.515884280204773},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.46585020422935486},{"id":"https://openalex.org/keywords/newtons-method","display_name":"Newton's method","score":0.454872727394104},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.4309194087982178},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4190613925457001},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4120786190032959},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3896259069442749},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3805100917816162},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.34126341342926025},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.3400508165359497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21426531672477722},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.07408785820007324},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.06943318247795105}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.7493717670440674},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7224382758140564},{"id":"https://openalex.org/C17902559","wikidata":"https://www.wikidata.org/wiki/Q17099734","display_name":"Restricted isometry property","level":3,"score":0.7205647230148315},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7104323506355286},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5342739224433899},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.515884280204773},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.46585020422935486},{"id":"https://openalex.org/C85189116","wikidata":"https://www.wikidata.org/wiki/Q374195","display_name":"Newton's method","level":3,"score":0.454872727394104},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.4309194087982178},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4190613925457001},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4120786190032959},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3896259069442749},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3805100917816162},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.34126341342926025},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3400508165359497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21426531672477722},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.07408785820007324},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.06943318247795105},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2020.3037996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.3037996","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2001.07181","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.07181","pdf_url":"https://arxiv.org/pdf/2001.07181","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,"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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2001.07181","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.07181","pdf_url":"https://arxiv.org/pdf/2001.07181","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,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3437049041","display_name":"\u53d8\u91cf\u90e8\u5206\u7a00\u758f\u6b63\u5219\u5316\u7b97\u6cd5\u8bbe\u8ba1\u4e0e\u7814\u7a76","funder_award_id":"11771003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4205807689","display_name":"\u6570\u636e\u538b\u7f29\u4e0e\u91cd\u6784\u7684\u65b0\u4e00\u4ee3\u9ad8\u6027\u80fd\u4f18\u5316\u7b97\u6cd5","funder_award_id":"12071307","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W119997944","https://openalex.org/W143004564","https://openalex.org/W340244495","https://openalex.org/W417781317","https://openalex.org/W1574645113","https://openalex.org/W1591116419","https://openalex.org/W1977520307","https://openalex.org/W1980454827","https://openalex.org/W1981487911","https://openalex.org/W2005969771","https://openalex.org/W2028781966","https://openalex.org/W2038517753","https://openalex.org/W2078204800","https://openalex.org/W2099766834","https://openalex.org/W2107861471","https://openalex.org/W2108982210","https://openalex.org/W2123023890","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2135479785","https://openalex.org/W2145096794","https://openalex.org/W2146842127","https://openalex.org/W2148556090","https://openalex.org/W2158940042","https://openalex.org/W2160979406","https://openalex.org/W2289917018","https://openalex.org/W2296052754","https://openalex.org/W2296616510","https://openalex.org/W2480981092","https://openalex.org/W2540784894","https://openalex.org/W2550629656","https://openalex.org/W2556237558","https://openalex.org/W2777462290","https://openalex.org/W2799167007","https://openalex.org/W2874258323","https://openalex.org/W2903224795","https://openalex.org/W2952254379","https://openalex.org/W2954811775","https://openalex.org/W2962975464","https://openalex.org/W2963322354","https://openalex.org/W2963839538","https://openalex.org/W2964292648","https://openalex.org/W2995268158","https://openalex.org/W2996018207","https://openalex.org/W2998383793","https://openalex.org/W3034627166","https://openalex.org/W3102903867","https://openalex.org/W3126727012","https://openalex.org/W3161751243","https://openalex.org/W4214806317","https://openalex.org/W4238744307","https://openalex.org/W4250431399","https://openalex.org/W4252713891","https://openalex.org/W4254546220","https://openalex.org/W4300263211","https://openalex.org/W4301014524","https://openalex.org/W4302439355","https://openalex.org/W6697563226","https://openalex.org/W6704584212","https://openalex.org/W6721875038","https://openalex.org/W6746990450","https://openalex.org/W6758164983","https://openalex.org/W6771989614"],"related_works":["https://openalex.org/W3195902396","https://openalex.org/W3103371283","https://openalex.org/W1988451630","https://openalex.org/W4380434867","https://openalex.org/W2468414543","https://openalex.org/W2765324516","https://openalex.org/W2568938859","https://openalex.org/W1975505689","https://openalex.org/W2077520262","https://openalex.org/W1848313300"],"abstract_inverted_index":{"Sparse":[0],"signal":[1,59,107,140,147],"recovery":[2,108,148],"or":[3],"compressed":[4,137],"sensing":[5,123,138],"can":[6],"be":[7],"formulated":[8],"as":[9],"certain":[10],"sparse":[11,58,106],"optimization":[12,16,35],"problems.":[13,36],"The":[14,142,164],"classic":[15,30],"theory":[17],"indicates":[18],"that":[19,150,159],"the":[20,29,42,50,63,74,78,84,92,102,117,129,134,151,173],"Newton-like":[21,79],"method":[22,32],"often":[23],"has":[24],"a":[25,122],"numerical":[26,165],"advantage":[27],"over":[28],"gradient":[31],"for":[33,57,91,101],"nonlinear":[34],"In":[37],"this":[38],"paper,":[39],"we":[40],"propose":[41],"so-called":[43],"Newton-step-based":[44,51],"iterative":[45,65],"hard":[46,52,66,70],"thresholding":[47,53,67,71],"(NSIHT)":[48],"and":[49,69,98,139,176],"pursuit":[54,72],"(NSHTP)":[55],"algorithms":[56,76,94,111,155,169],"recovery.":[60],"Different":[61],"from":[62,145],"traditional":[64],"(IHT)":[68],"(HTP),":[73],"proposed":[75,93,154],"adopt":[77],"search":[80],"direction":[81],"instead":[82],"of":[83,105,116,121,128,136,153,160,167],"steepest":[85],"descent":[86],"direction.":[87],"A":[88],"theoretical":[89],"analysis":[90],"is":[95,126,179],"carried":[96],"out,":[97],"sufficient":[99],"conditions":[100],"guaranteed":[103],"success":[104],"via":[109],"these":[110],"are":[112,156],"established":[113],"in":[114,133],"terms":[115],"restricted":[118],"isometry":[119],"property":[120],"matrix":[124],"which":[125],"one":[127],"standard":[130],"assumptions":[131],"used":[132],"field":[135],"approximation.":[141],"empirical":[143],"results":[144],"synthetic":[146],"indicate":[149],"performance":[152],"comparable":[157],"to":[158,172],"several":[161],"existing":[162],"algorithms.":[163],"behavior":[166],"our":[168],"with":[170],"respect":[171],"residual":[174],"reduction":[175],"parameter":[177],"changes":[178],"also":[180],"investigated":[181],"through":[182],"simulations.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2020-01-30T00:00:00"}
