{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T16:50:07Z","timestamp":1765039807682,"version":"3.46.0"},"reference-count":42,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2025,12,1]]},"DOI":"10.1587\/transfun.2025eap1035","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T18:06:35Z","timestamp":1748801195000},"page":"1629-1642","source":"Crossref","is-referenced-by-count":1,"title":["An LiGME Regularizer of Designated Isolated Minimizers \u2014\u2006An Application to Discrete-Valued Signal Estimation"],"prefix":"10.1587","volume":"E108.A","author":[{"given":"Satoshi","family":"SHOJI","sequence":"first","affiliation":[{"name":"Department of Information and Communications Engineering, Institute of Science Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wataru","family":"YATA","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Institute of Science Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keita","family":"KUME","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Institute of Science Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isao","family":"YAMADA","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Institute of Science Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] W. Liu, N. Wang, M. Jin, and H. Xu, \u201cDenoising detection for the generalized spatial modulation system using sparse property,\u201d IEEE Commun. Lett., vol.18, no.1, pp.22-25, 2013. 10.1109\/lcomm.2013.111413.131722","DOI":"10.1109\/LCOMM.2013.111413.131722"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] H. Sasahara, K. Hayashi, and M. Nagahara, \u201cMultiuser detection based on MAP estimation with sum-of-absolute-values relaxation,\u201d IEEE Trans. Signal Process., vol.65, no.21, pp.5621-5634, 2017. 10.1109\/tsp.2017.2740164","DOI":"10.1109\/TSP.2017.2740164"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] H. Zhu and G.B. Giannakis, \u201cExploiting sparse user activity in multiuser detection,\u201d IEEE Trans. Commun., vol.59, no.2, pp.454-465, 2010. 10.1109\/tcomm.2011.121410.090570","DOI":"10.1109\/TCOMM.2011.121410.090570"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] E. Axell, G. Leus, E.G. Larsson, and H.V. Poor, \u201cSpectrum sensing for cognitive radio: State-of-the-art and recent advances,\u201d IEEE Signal Process. Mag., vol.29, no.3, pp.101-116, 2012. 10.1109\/msp.2012.2183771","DOI":"10.1109\/MSP.2012.2183771"},{"key":"5","unstructured":"[5] M. Nikolova, \u201cEstimation of binary images by minimizing convex criteria,\u201d 1998 IEEE Int. Conf. Image Process. ICIP, 1998. 10.1109\/icip.1998.723327"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] M.F. Duarte, M.A. Davenport, D. Takhar, J.N. Laska, T. Sun, K.F. Kelly, and R.G. Baraniuk, \u201cSingle-pixel imaging via compressive sampling,\u201d IEEE Signal Process. Mag., vol.25, no.2, pp.83-91, 2008. 10.1109\/msp.2007.914730","DOI":"10.1109\/MSP.2007.914730"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] V. Bioglio, G. Coluccia, and E. Magli, \u201cSparse image recovery using compressed sensing over finite alphabets,\u201d 2014 IEEE Int. Conf. Image Process. ICIP, pp.1287-1291, 2014. 10.1109\/icip.2014.7025257","DOI":"10.1109\/ICIP.2014.7025257"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] A. Tuysuzoglu, W.C. Karl, I. Stojanovic, D. Casta\u00f1\u00f2n, and M.S. \u00dcnl\u00fc, \u201cGraph-cut based discrete-valued image reconstruction,\u201d IEEE Trans. Image Process., vol.24, no.5, pp.1614-1627, 2015. 10.1109\/tip.2015.2409568","DOI":"10.1109\/TIP.2015.2409568"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] P. Sarangi and P. Pal, \u201cMeasurement matrix design for sample-efficient binary compressed sensing,\u201d IEEE Signal Process. Lett., vol.29, pp.1307-1311, 2022. 10.1109\/lsp.2022.3179230","DOI":"10.1109\/LSP.2022.3179230"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] S. Verd\u00fa, \u201cComputational complexity of optimum multiuser detection,\u201d Algorithmica, vol.4, no.1-4, pp.303-312, 1989. 10.1007\/bf01553893","DOI":"10.1007\/BF01553893"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] M. Wu, C. Dick, J.R. Cavallaro, and C. Studer, \u201cHigh-throughput data detection for massive MU-MIMO-OFDM using coordinate descent,\u201d IEEE Trans. Circuits Syst. I, Reg. Papers, vol.63, no.12, pp.2357-2367, 2016. 10.1109\/tcsi.2016.2611645","DOI":"10.1109\/TCSI.2016.2611645"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] A. Kudeshia, A.K. Jagannatham, and L. Hanzo, \u201cTotal variation based joint detection and state estimation for wireless communication in smart grids,\u201d IEEE Access, vol.7, pp.31598-31614, 2019. 10.1109\/access.2019.2902325","DOI":"10.1109\/ACCESS.2019.2902325"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] J.C. Chen, \u201cManifold optimization approach for data detection in massive multiuser MIMO systems,\u201d IEEE Trans. Veh. Technol., vol.67, no.4, pp.3652-3657, 2017. 10.1109\/tvt.2017.2779157","DOI":"10.1109\/TVT.2017.2779157"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] A. Elgabli, A. Elghariani, A.O. Al-Abbasi, and M. Bell, \u201cTwo-stage LASSO ADMM signal detection algorithm for large scale MIMO,\u201d 2017 51st Asilomar Conf. Signals Syst. Comput., pp.1660-1664, IEEE, 2017. 10.1109\/acssc.2017.8335641","DOI":"10.1109\/ACSSC.2017.8335641"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] R. Hayakawa and K. Hayashi, \u201cConvex optimization-based signal detection for massive overloaded MIMO systems,\u201d IEEE Trans. Wireless Commun., vol.16, no.11, pp.7080-7091, 2017. 10.1109\/twc.2017.2739140","DOI":"10.1109\/TWC.2017.2739140"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] H. Iimori, G.T.F. De Abreu, T. Hara, K. Ishibashi, R.A. Stoica, D. Gonz\u00e1lez, and O. Gonsa, \u201cRobust symbol detection in large-scale overloaded NOMA systems,\u201d IEEE Open J. Commun. Soc., vol.2, pp.512-533, 2021. 10.1109\/ojcoms.2021.3064983","DOI":"10.1109\/OJCOMS.2021.3064983"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] M. Nagahara, \u201cDiscrete signal reconstruction by sum of absolute values,\u201d IEEE Signal Process. Lett., vol.22, no.10, pp.1575-1579, 2015. 10.1109\/lsp.2015.2414932","DOI":"10.1109\/LSP.2015.2414932"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] R. Hayakawa and K. Hayashi, \u201cDiscrete-valued vector reconstruction by optimization with sum of sparse regularizers,\u201d 2019 27th Eur. Signal Process. Conf. EUSIPCO, pp.1-5, IEEE, 2019. 10.23919\/eusipco.2019.8902940","DOI":"10.23919\/EUSIPCO.2019.8902940"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[20] C.H. Zhang, \u201cNearly unbiased variable selection under minimax concave penalty,\u201d Ann. Statist., vol.38, no.2, pp.894-942, 2010. 10.1214\/09-aos729","DOI":"10.1214\/09-AOS729"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[21] I. Selesnick, \u201cSparse regularization via convex analysis,\u201d IEEE Trans. Signal Process., vol.65, no.17, pp.4481-4494, 2017. 10.1109\/tsp.2017.2711501","DOI":"10.1109\/TSP.2017.2711501"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[22] J. Abe, M. Yamagishi, and I. Yamada, \u201cLinearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition,\u201d Inverse Probl., vol.36, no.3, p.035012, 2020. 10.1088\/1361-6420\/ab551e","DOI":"10.1088\/1361-6420\/ab551e"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[23] W. Yata, M. Yamagishi, and I. Yamada, \u201cA constrained LiGME model and its proximal splitting algorithm under overall convexity condition,\u201d J. Appl. Numer. Optim., vol.4, no.2, pp.245-271, 2022. 10.23952\/jano.4.2022.2.09","DOI":"10.23952\/jano.4.2022.2.09"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[24] D. Kitahara, R. Kato, H. Kuroda, and A. Hirabayashi, \u201cMulti-contrast CSMRI using common edge structures with LiGME model,\u201d 2021 29th Eur. Signal Process. Conf. EUSIPCO, pp.2119-2123, IEEE, 2021. 10.23919\/eusipco54536.2021.9616083","DOI":"10.23919\/EUSIPCO54536.2021.9616083"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[25] W. Yata and I. Yamada, \u201cImposing early and asymptotic constraints on LiGME with application to bivariate nonconvex enhancement of fused lasso models,\u201d arXiv preprint, arXiv:2309.14082, 2024. 10.48550\/arXiv.2309.14082","DOI":"10.1109\/ICASSP48485.2024.10446039"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[26] R. Hayakawa and K. Hayashi, \u201cReconstruction of complex discrete-valued vector via convex optimization with sparse regularizers,\u201d IEEE Access, vol.6, pp.66499-66512, 2018. 10.1109\/access.2018.2878886","DOI":"10.1109\/ACCESS.2018.2878886"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[27] Y. Censor, R. Davidi, and G.T. Herman, \u201cPerturbation resilience and superiorization of iterative algorithms,\u201d Inverse Probl., vol.26, no.6, p.065008, 2010. 10.1088\/0266-5611\/26\/6\/065008","DOI":"10.1088\/0266-5611\/26\/6\/065008"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[28] J. Fink, R.L.G. Cavalcante, and S. Sta\u0144czak, \u201cSuperiorized adaptive projected subgradient method with application to MIMO detection,\u201d IEEE Trans. Signal Process., vol.71, pp.1350-1362, 2023. 10.1109\/tsp.2023.3263255","DOI":"10.1109\/TSP.2023.3263255"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[29] S. Shoji, W. Yata, K. Kume, and I. Yamada, \u201cA discrete-valued signal estimation by nonconvex enhancement of SOAV with cLiGME model,\u201d 2024 Asia-Pac. Signal Inf. Process. Assoc. Annu. Summit Conf. APSIPA ASC, 2024. 10.1109\/apsipaasc63619.2025.10848771","DOI":"10.1109\/APSIPAASC63619.2025.10848771"},{"key":"29","unstructured":"[30] D.G. Luenberger, Optimization by Vector Space Methods, John Wiley &amp; Sons, 1997."},{"key":"30","unstructured":"[31] I. Yamada, Kougaku no Tameno Kansu Kaiseki (Functional Analysis for Engineering), Suurikougaku-Sha\/Saiensu-Sha, Tokyo, 2009."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[32] H.H. Bauschke and P.L. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, CMS Books in Mathematics, Springer International Publishing, Cham, 2017. 10.1007\/978-3-319-48311-5","DOI":"10.1007\/978-3-319-48311-5"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[34] Y. Chen, M. Yamagishi, and I. Yamada, \u201cA unified design of generalized Moreau enhancement matrix for sparsity aware LiGME models,\u201d IEICE Trans. Fundamentals, vol.E106-A, no.8, pp.1025-1036, Aug. 2023. 10.1587\/transfun.2022eap1118","DOI":"10.1587\/transfun.2022EAP1118"},{"key":"33","unstructured":"[35] W. Yata, K. Kume, and I. Yamada, \u201cLinearly involved generalized Moreau enhanced model with non-quadratic smooth convex data fidelity functions,\u201d arXiv:2509.03258, 2025. 10.48550\/arXiv.2509.03258"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[36] R. Tibshirani, \u201cRegression shrinkage and selection via the lasso,\u201d J.R. Stat. Soc. Ser. B Stat. Methodol., vol.58, no.1, pp.267-288, 1996. 10.1111\/j.2517-6161.1996.tb02080.x","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[37] M. Kowalski, \u201cSparse regression using mixed norms,\u201d Appl. Comput. Harmon. Anal., vol.27, no.3, pp.303-324, 2009. 10.1016\/j.acha.2009.05.006","DOI":"10.1016\/j.acha.2009.05.006"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[38] K.K. Wong, A. Paulraj, and R.D. Murch, \u201cEfficient high-performance decoding for overloaded MIMO antenna systems,\u201d IEEE Trans. Wireless Commun., vol.6, no.5, pp.1833-1843, 2007. 10.1109\/twc.2007.360385","DOI":"10.1109\/TWC.2007.360385"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[39] T. Mizoguchi and I. Yamada, \u201cHypercomplex tensor completion via convex optimization,\u201d IEEE Trans. Signal Process., vol.67, no.15, pp.4078-4092, 2019. 10.1109\/tsp.2019.2922156","DOI":"10.1109\/TSP.2019.2922156"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[40] O. Casta\u00f1eda, S. Jacobsson, G. Durisi, T. Goldstein, and C. Studer, \u201cVLSI design of a 3-bit constant-modulus precoder for massive MU-MIMO,\u201d 2018 IEEE Int. Symp. Circuits Syst. ISCAS, 2018. 10.1109\/iscas.2018.8351894","DOI":"10.1109\/ISCAS.2018.8351894"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[41] P.L. Combettes and V.R. Wajs, \u201cSignal recovery by proximal forward-backward splitting,\u201d Multiscale Model. Simul., vol.4, no.4, pp.1168-1200, 2005. 10.1137\/050626090","DOI":"10.1137\/050626090"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[42] N. Ogura and I. Yamada, \u201cNon-strictly convex minimization over the fixed point set of an asymptotically shrinking nonexpansive mapping,\u201d Numer. Funct. Anal. Optim., vol.23, no.1-2, pp.113-137, 2002. 10.1081\/nfa-120003674","DOI":"10.1081\/NFA-120003674"},{"key":"41","doi-asserted-by":"publisher","unstructured":"[43] P.L. Combettes and I. Yamada, \u201cCompositions and convex combinations of averaged nonexpansive operators,\u201d J. Math. Anal. Appl., vol.425, no.1, pp.55-70, 2015. 10.1016\/j.jmaa.2014.11.044","DOI":"10.1016\/j.jmaa.2014.11.044"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[44] E.J. Cand\u00e8s, M.B. Wakin, and S.P. Boyd, \u201cEnhancing sparsity by reweighted \u2113<sub>1<\/sub> minimization,\u201d J. Fourier Anal. Appl., vol.14, pp.877-905, 2008. 10.1007\/s00041-008-9045-x","DOI":"10.1007\/s00041-008-9045-x"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/12\/E108.A_2025EAP1035\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T03:24:57Z","timestamp":1764991497000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/12\/E108.A_2025EAP1035\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":42,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2025eap1035","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"type":"print","value":"0916-8508"},{"type":"electronic","value":"1745-1337"}],"subject":[],"published":{"date-parts":[[2025,12,1]]},"article-number":"2025EAP1035"}}