{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:00:40Z","timestamp":1740171640990,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>There has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc. While there have been extensive theory and theoretical advances, a proportionally small body of scientific literature focuses on numerical evaluation of the proposed methods in actual practical, parallel programming environments. This paper considers a general algorithmic framework of first and second order methods with sparsified communications and computations across worker nodes. The considered framework subsumes several existing methods. In addition, a novel method that utilizes unidirectional sparsified communications is introduced and theoretical convergence analysis is also provided. Namely, we prove R-linear convergence in the expected norm. A thorough empirical evaluation of the methods using Message Passing Interface (MPI) on a High Performance Computing (HPC) cluster is carried out and several useful insights and guidelines on the performance of algorithms and inherent communication-computational trade-offs in a realistic setting are derived.<\/jats:p>","DOI":"10.1186\/s13634-021-00736-4","type":"journal-article","created":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T15:34:29Z","timestamp":1622561669000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified directed communication"],"prefix":"10.1186","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8199-7767","authenticated-orcid":false,"given":"Lidija","family":"Fodor","sequence":"first","affiliation":[]},{"given":"Du\u0161an","family":"Jakoveti\u0107","sequence":"additional","affiliation":[]},{"given":"Nata\u0161a","family":"Kreji\u0107","sequence":"additional","affiliation":[]},{"given":"Nata\u0161a Krklec","family":"Jerinki\u0107","sequence":"additional","affiliation":[]},{"given":"Sr\u0111an","family":"\u0160krbi\u0107","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,1]]},"reference":[{"issue":"1","key":"736_CR1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/TAC.2008.2009515","volume":"54","author":"A. Nedic","year":"2009","unstructured":"A. Nedic, A. Ozdaglar, Distributed subgradient methods for multi-agent optimization. IEEE Trans. Autom. Control. 54(1), 48\u201361 (2009). https:\/\/doi.org\/10.1109\/tac.2008.2009515.","journal-title":"IEEE Trans. Autom. Control"},{"issue":"3","key":"736_CR2","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/s10957-010-9737-7","volume":"147","author":"S. S. Ram","year":"2010","unstructured":"S. S. Ram, A. Nedich, V. V. Veeravalli, Distributed stochastic subgradient projection algorithms for convex optimization. J. Optim. Theory Appl.147(3), 516\u2013545 (2010). https:\/\/doi.org\/10.1007\/s10957-010-9737-7.","journal-title":"J. Optim. Theory Appl."},{"issue":"5","key":"736_CR3","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1109\/TAC.2014.2298712","volume":"59","author":"D. Jakovetic","year":"2014","unstructured":"D. Jakovetic, J. M. F. Xavier, J. M. F. Moura, Fast distributed gradient methods. IEEE Trans. Autom. Control. 59(5), 1131\u20131146 (2014). https:\/\/doi.org\/10.1109\/tac.2014.2298712.","journal-title":"IEEE Trans. Autom. Control"},{"issue":"1","key":"736_CR4","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TSP.2016.2617829","volume":"65","author":"A. Mokhtari","year":"2017","unstructured":"A. Mokhtari, Q. Ling, A. Ribeiro, Network newton distributed optimization methods. IEEE Trans. Signal Process.65(1), 146\u2013161 (2017). https:\/\/doi.org\/10.1109\/tsp.2016.2617829.","journal-title":"IEEE Trans. Signal Process."},{"issue":"2","key":"736_CR5","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1137\/15M1038049","volume":"27","author":"D. Bajovi\u0107","year":"2017","unstructured":"D. Bajovi\u0107, D. Jakoveti\u0107, N. Kreji\u0107, N. Krklec Jerinki\u0107, Newton-like method with diagonal correction for distributed optimization. SIAM J. Optim.27(2), 1171\u20131203 (2017). https:\/\/doi.org\/110.1137\/15m1038049.","journal-title":"SIAM J. Optim."},{"key":"736_CR6","unstructured":"A. Mokhtari, Q. Ling, A. Ribeiro, Network newton-part II: Convergence rate and implementation. arXiv: Optimization and Control (2015). arXiv preprint arXiv:1504.06020."},{"key":"736_CR7","unstructured":"K. Zhang, Z. Yang, H. Liu, T. Zhang, T. Basar, in Proceedings of the 35th International Conference on Machine Learning, vol. 80, ed. by J. Dy, A. Krause. Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (PMLR, 2018), pp. 5872\u20135881."},{"key":"736_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/9780470724200","volume-title":"Cooperative Control of Distributed Multi-Agent Systems","author":"J. Shamma","year":"2008","unstructured":"J. Shamma, Cooperative Control of Distributed Multi-Agent Systems (Wiley-Interscience, USA, 2008). https:\/\/doi.org\/10.1002\/9780470724200."},{"key":"736_CR9","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1109\/WIIAT.2008.88","volume-title":"2008 IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology","author":"A. Salkham","year":"2008","unstructured":"A. Salkham, R. Cunningham, A. Garg, V. Cahill, in 2008 IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2. A collaborative reinforcement learning approach to urban traffic control optimization (IEEESydney, NSW, Australia, 2008), pp. 560\u2013566. https:\/\/doi.org\/10.1109\/WIIAT.2008.88."},{"key":"736_CR10","doi-asserted-by":"publisher","first-page":"3341","DOI":"10.1109\/IECON.2010.5675295","volume-title":"IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society","author":"R. Roche","year":"2010","unstructured":"R. Roche, B. Blunier, A. Miraoui, V. Hilaire, A. Koukam, in IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society. Multi-agent systems for grid energy management: A short review (IEEEGlendale, 2010), pp. 3341\u20133346. https:\/\/doi.org\/10.1109\/IECON.2010.5675295."},{"issue":"1","key":"736_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S. Boyd","year":"2011","unstructured":"S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn.3(1), 1\u2013122 (2011). https:\/\/doi.org\/10.1561\/2200000016.","journal-title":"Found. Trends Mach. Learn."},{"issue":"15","key":"736_CR12","doi-asserted-by":"publisher","first-page":"4080","DOI":"10.1109\/TSP.2016.2560133","volume":"64","author":"D. Jakoveti\u0107","year":"2016","unstructured":"D. Jakoveti\u0107, D. Bajovi\u0107, N. Kreji\u0107, N. Krklec Jerinki\u0107, Distributed gradient methods with variable number of working nodes. IEEE Trans. Signal Process.64(15), 4080\u20134095 (2016). https:\/\/doi.org\/10.1109\/TSP.2016.2560133.","journal-title":"IEEE Trans. Signal Process."},{"key":"736_CR13","doi-asserted-by":"crossref","unstructured":"A. K. Sahu, D. Jakovetic, D. Bajovic, S. Kar, Communication-efficient distributed strongly convex stochastic optimization: Non-asymptotic rates (2018). http:\/\/arxiv.org\/abs\/arXiv:1809.02920.","DOI":"10.1109\/GlobalSIP.2018.8646406"},{"key":"736_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/EUROCON.2019.8861544","volume-title":"IEEE EUROCON 2019 -18th International Conference on Smart Technologies","author":"A. K. Sahu","year":"2019","unstructured":"A. K. Sahu, D. Jakovetic, D. Bajovic, S. Kar, in IEEE EUROCON 2019 -18th International Conference on Smart Technologies. Communication Efficient Distributed Estimation Over Directed Random Graphs (IEEENovi Sad, 2019), pp. 1\u20135. https:\/\/doi.org\/10.1109\/EUROCON.2019.8861544."},{"key":"736_CR15","doi-asserted-by":"publisher","first-page":"4238","DOI":"10.1109\/CDC.2018.8619228","volume-title":"2018 IEEE Conference on Decision and Control (CDC)","author":"D. Jakoveti\u0107","year":"2018","unstructured":"D. Jakoveti\u0107, D. Bajovi\u0107, A. K. Sahu, S. Kar, in 2018 IEEE Conference on Decision and Control (CDC). Convergence Rates for Distributed Stochastic Optimization Over Random Networks (IEEEMiami Beach, 2018), pp. 4238\u20134245. https:\/\/doi.org\/10.1109\/CDC.2018.8619228."},{"issue":"2","key":"736_CR16","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1109\/TAC.2019.2922191","volume":"65","author":"N. Krklec Jerinki\u0107","year":"2020","unstructured":"N. Krklec Jerinki\u0107, D. Jakoveti\u0107, N. Kreji\u0107, D. Bajovi\u0107, Distributed Second-Order Methods With Increasing Number of Working Nodes. IEEE Trans. Autom. Control.65(2), 846\u2013853 (2020). https:\/\/doi.org\/10.1109\/tac.2019.2922191.","journal-title":"IEEE Trans. Autom. Control."},{"key":"736_CR17","doi-asserted-by":"publisher","first-page":"4951","DOI":"10.1109\/CDC.2018.8619044","volume-title":"2018 IEEE Conference on Decision and Control (CDC)","author":"A. Sahu","year":"2018","unstructured":"A. Sahu, D. Jakoveti\u0107, D. Bajovi\u0107, S. Kar, in 2018 IEEE Conference on Decision and Control (CDC). Distributed Zeroth Order Optimization Over Random Networks: A Kiefer-Wolfowitz Stochastic Approximation Approach (IEEEMiami Beach, 2018), pp. 4951\u20134958. https:\/\/doi.org\/10.1109\/cdc.2018.8619044."},{"issue":"SI","key":"736_CR18","doi-asserted-by":"publisher","first-page":"2508","DOI":"10.1109\/TIT.2006.874516","volume":"14","author":"S. Boyd","year":"2006","unstructured":"S. Boyd, A. Ghosh, B. Prabhakar, D. Shah, Randomized gossip algorithms. IEEE\/ACM Trans. Netw.14(SI), 2508\u20132530 (2006). https:\/\/doi.org\/10.1109\/TIT.2006.874516.","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"736_CR19","volume-title":"MPI: A Message-passing Interface Standard, Version 3.1","author":"Message Passing Interface Forum","year":"2015","unstructured":"Message Passing Interface Forum, MPI: A Message-passing Interface Standard, Version 3.1 (High-Performance Computing Center Stuttgart, University of Stuttgart, 2015)."},{"key":"736_CR20","first-page":"1943","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS\u201312","author":"K. I. Tsianos","year":"2012","unstructured":"K. I. Tsianos, S. F. Lawlor, M. G. Rabbat, in Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS\u201312, 2. Communication\/computation tradeoffs in consensus-based distributed optimization (Curran Associates Inc.Red Hook, NY, USA, 2012), pp. 1943\u20131951."},{"issue":"2","key":"736_CR21","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1137\/140954362","volume":"26","author":"R. H. Byrd","year":"2016","unstructured":"R. H. Byrd, S. L. Hansen, J. Nocedal, Y. Singer, A stochastic quasi-newton method for large-scale optimization. SIAM J. Optim.26(2), 1008\u20131031 (2016). https:\/\/doi.org\/10.1137\/140954362.","journal-title":"SIAM J. Optim."},{"key":"736_CR22","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/Allerton.2012.6483273","volume-title":"2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)","author":"I. A. Chen","year":"2012","unstructured":"I. A. Chen, A. Ozdaglar, in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). A fast distributed proximal-gradient method (IEEEMonticello, 2012), pp. 601\u2013608. https:\/\/doi.org\/10.1109\/Allerton.2012.6483273."},{"issue":"3","key":"736_CR23","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1137\/08073038X","volume":"20","author":"B. Johansson","year":"2009","unstructured":"B. Johansson, M. Rabi, M. Johansson, A randomized incremental subgradient method for distributed optimization in networked systems. SIAM J. Optim.20(3), 1157\u20131170 (2009). https:\/\/doi.org\/10.1137\/08073038x.","journal-title":"SIAM J. Optim."},{"issue":"5","key":"736_CR24","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1109\/JPROC.2018.2817461","volume":"106","author":"A. Nedi\u0107","year":"2018","unstructured":"A. Nedi\u0107, A. Olshevsky, M. G. Rabbat, Network topology and communication-computation tradeoffs in decentralized optimization. Proc. IEEE. 106(5), 953\u2013976 (2018). https:\/\/doi.org\/10.1109\/JPROC.2018.2817461.","journal-title":"Proc. IEEE"},{"key":"736_CR25","unstructured":"M. Assran, M. Rabbat, Asynchronous subgradient-push. Computing Research Repository, CoRR (2018). abs\/1803.08950(2018). arXiv:1803.08950."},{"key":"736_CR26","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1109\/GlobalSIP.2017.8309024","volume-title":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","author":"M. Assran","year":"2017","unstructured":"M. Assran, M. Rabbat, in 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). An empirical comparison of multi-agent optimization algorithms (IEEEMontr\u00e9al, 2017), pp. 573\u2013577. https:\/\/doi.org\/10.1109\/GlobalSIP.2017.8309024."},{"issue":"6","key":"736_CR27","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TAC.2019.2930234","volume":"65","author":"J. Zhang","year":"2020","unstructured":"J. Zhang, K. You, AsySPA: An exact asynchronous algorithm for convex optimization over digraphs. IEEE Trans. Autom. Control. 65(6), 2494\u20132509 (2020). https:\/\/doi.org\/10.1109\/tac.2019.2930234.","journal-title":"IEEE Trans. Autom. Control"},{"key":"736_CR28","doi-asserted-by":"publisher","unstructured":"K. I. Tsianos, S. Lawlor, M. G. Rabbat, in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning, (2012), pp. 1543\u20131550. https:\/\/doi.org\/10.1109\/Allerton.2012.6483403.","DOI":"10.1109\/Allerton.2012.6483403"},{"issue":"3","key":"736_CR29","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1137\/130943170","volume":"26","author":"K. Yuan","year":"2016","unstructured":"K. Yuan, Q. Ling, W. Yin, On the convergence of decentralized gradient descent. SIAM J. Optim.26(3), 1835\u20131854 (2016). https:\/\/doi.org\/10.1137\/130943170.","journal-title":"SIAM J. Optim."},{"key":"736_CR30","doi-asserted-by":"publisher","unstructured":"D. Jakoveti\u0107, J. M. F. Moura, J. Xavier, in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). Distributed nesterov-like gradient algorithms, (2012), pp. 5459\u20135464. https:\/\/doi.org\/10.1109\/CDC.2012.6425938.","DOI":"10.1109\/CDC.2012.6425938"},{"issue":"3","key":"736_CR31","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/s10957-010-9737-7","volume":"147","author":"S. Sundhar Ram","year":"2010","unstructured":"S. Sundhar Ram, A. Nedi\u0107, V. V. Veeravalli, Distributed stochastic subgradient projection algorithms for convex optimization. J. Optim. Theory Appl.147(3), 516\u2013545 (2010). https:\/\/doi.org\/10.1007\/s10957-010-9737-7.","journal-title":"J. Optim. Theory Appl."},{"key":"736_CR32","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719604","volume-title":"LAPACK UsersG\u0301uide","author":"E. Anderson","year":"1999","unstructured":"E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. D. Croz, A. Greenbaum, S. Hammarling, A. McKenney, D. Sorensen, LAPACK UsersG\u0301uide, 3rd edn. (Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, USA, 1999). https:\/\/doi.org\/10.1137\/1.9780898719604."},{"issue":"2","key":"736_CR33","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1145\/567806.567807","volume":"28","author":"L. Blackford","year":"2002","unstructured":"L. Blackford, et al., An updated set of basic linear algebra subprograms (BLAS). ACM Trans. Math. Softw.28(2), 135\u2013151 (2002). https:\/\/doi.org\/10.1145\/567806.567807.","journal-title":"ACM Trans. Math. Softw."},{"key":"736_CR34","unstructured":"E. F. Tjong Kim Sang, F. De Meulder, Language-Independent Named Entity Recognition (II) (2005). https:\/\/www.clips.uantwerpen.be\/conll2003\/ner\/. Accessed 30 May 2019."},{"key":"736_CR35","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3115\/1119176.1119195","volume-title":"Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, CONLL \u201303","author":"E. F. Tjong Kim Sang","year":"2003","unstructured":"E. F. Tjong Kim Sang, F. De Meulder, in Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, CONLL \u201303, 4. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition (Association for Computational LinguisticsUSA, 2003), pp. 142\u2013147. https:\/\/doi.org\/10.3115\/1119176.1119195."},{"key":"736_CR36","unstructured":"I. Guyon, UCI Machine Learning Repository, Gisette Data Set (2008). http:\/\/archive.ics.uci.edu\/ml\/datasets\/gisette. Accessed 29 May 2019."},{"key":"736_CR37","unstructured":"D. Dua, C. Graff, UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2017). http:\/\/archive.ics.uci.edu\/ml. Accessed 29 May 2019."},{"key":"736_CR38","first-page":"545","volume-title":"Proceedings of the 17th International Conference on Neural Information Processing Systems, NIPS\u201304","author":"I. Guyon","year":"2004","unstructured":"I. Guyon, S. Gunn, A. Ben-Hur, G. Dror, in Proceedings of the 17th International Conference on Neural Information Processing Systems, NIPS\u201304, 17. Result analysis of the NIPS 2003 feature selection challenge (MIT PressCambridge, MA, USA, 2004), pp. 545\u2013552. https:\/\/eprints.soton.ac.uk\/261923\/."},{"key":"736_CR39","unstructured":"T. Bertin-Mahieux, UCI Machine Learning Repository, YearPredictionMSD data set (2011). https:\/\/archive.ics.uci.edu\/ml\/datasets\/YearPredictionMSD. Accessed 01 Sept 2019."},{"key":"736_CR40","doi-asserted-by":"publisher","first-page":"591","DOI":"10.7916\/D8NZ8J07","volume-title":"Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011)","author":"T. Bertin-Mahieux","year":"2011","unstructured":"T. Bertin-Mahieux, D. P. W. Ellis, B. Whitman, P. Lamere, in Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011). The Million Song Dataset (University of MiamiMiami, 2011), pp. 591\u2013596. https:\/\/doi.org\/10.7916\/D8NZ8J07."},{"key":"736_CR41","unstructured":"Y. LeCun, C. Cortes, \u201cMNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges\u201d, THE MNIST DATABASE of handwritten digits (2005). http:\/\/yann.lecun.com\/exdb\/mnist\/. Accessed 01 Sept 2019."},{"key":"736_CR42","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L. Deng","year":"2012","unstructured":"L. Deng, The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Proc. Mag.29:, 141\u2013142 (2012). https:\/\/doi.org\/10.1109\/MSP.2012.2211477.","journal-title":"IEEE Signal Proc. Mag."},{"key":"736_CR43","unstructured":"F. Graf, H. -P. Kriegel, M. Schubert, S. Poelsterl, A. Cavallaro, UCI Machine Learning Repository: Relative location of CT slices on axial axis Data Set (2011). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Relative+location+of+CT+slices+on+axial+axis. Accessed 08 Sept 2019."},{"key":"736_CR44","first-page":"607","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 6892","author":"F. Graf","year":"2011","unstructured":"F. Graf, H. -P. Kriegel, M. Schubert, S. P\u00f6lsterl, A. Cavallaro, in International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 6892. 2d image registration in CT images using radial image descriptors (SpringerToronto, 2011), pp. 607\u2013614."},{"key":"736_CR45","unstructured":"UCI Machine Learning Repository, p53 Mutants Data Set. (2010; accessed on: September 03, 2019). https:\/\/archive.ics.uci.edu\/ml\/datasets\/p53+Mutants."},{"key":"736_CR46","doi-asserted-by":"publisher","first-page":"1000498","DOI":"10.1371\/journal.pcbi.1000498","volume":"5","author":"S. Danziger","year":"2009","unstructured":"S. Danziger, R. Baronio, L. Ho, L. Hall, K. Salmon, G. Hatfield, P. Kaiser, R. Lathrop, Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning. PLoS Comput. Biol.5:, 1000498 (2009). https:\/\/doi.org\/10.1371\/journal.pcbi.1000498.","journal-title":"PLoS Comput. Biol."},{"issue":"13","key":"736_CR47","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1093\/bioinformatics\/btm166","volume":"23","author":"S. A. Danziger","year":"2007","unstructured":"S. A. Danziger, J. Zeng, Y. Wang, R. K. Brachmann, R. H. Lathrop, Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants. Bioinformatics. 23(13), 104\u2013114 (2007). https:\/\/doi.org\/10.1093\/bioinformatics\/btm166.","journal-title":"Bioinformatics"},{"key":"736_CR48","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TCBB.2006.22","volume":"3","author":"S. Danziger","year":"2006","unstructured":"S. Danziger, S. J. Swamidass, J. Zeng, L. Dearth, Q. Lu, J. Chen, J. Cheng, V. Hoang, H. Saigo, R. Luo, P. Baldi, R. Brachmann, R. Lathrop, Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants. IEEE\/ACM Trans. Comput. Biol. Bioinform.3:, 114\u201325 (2006). https:\/\/doi.org\/10.1109\/TCBB.2006.22.","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"2","key":"736_CR49","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s101070100263","volume":"91","author":"E. D. Dolan","year":"2002","unstructured":"E. D. Dolan, J. J. Mor\u00e9, Benchmarking optimization software with performance profiles. Math. Program.91(2), 201\u2013213 (2002). https:\/\/doi.org\/10.1007\/s101070100263.","journal-title":"Math. Program."},{"key":"736_CR50","unstructured":"W. -S. Zhang, \u201cGitHub-HaidYi\/admm-l1-2-logistic-regression: ADMM l1\/2\u201d logistic reression using MPI and GSL\u201d, HaidYi\/admm-l1-2-logistic-regression. GitHub repository. https:\/\/github.com\/HaidYi\/admm-l1-2-logistic-regression. Accessed 15 May 2020."}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-021-00736-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-021-00736-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-021-00736-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T15:42:03Z","timestamp":1622562123000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-021-00736-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,1]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["736"],"URL":"https:\/\/doi.org\/10.1186\/s13634-021-00736-4","relation":{},"ISSN":["1687-6180"],"issn-type":[{"type":"electronic","value":"1687-6180"}],"subject":[],"published":{"date-parts":[[2021,6,1]]},"assertion":[{"value":"19 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"25"}}