[{"project":[{"call_identifier":"FP7","name":"Discrete Optimization in Computer Vision: Theory and Practice","_id":"25FBA906-B435-11E9-9278-68D0E5697425","grant_number":"616160"}],"quality_controlled":"1","date_published":"2019-06-01T00:00:00Z","_id":"7468","publication_identifier":{"issn":["1063-6919"],"isbn":["9781728132938"]},"department":[{"_id":"VlKo"}],"date_created":"2020-02-09T23:00:52Z","publisher":"IEEE","citation":{"chicago":"Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate Frank-Wolfe Algorithm.” In <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>, Vol. 2019–June. IEEE, 2019. <a href=\"https://doi.org/10.1109/CVPR.2019.01140\">https://doi.org/10.1109/CVPR.2019.01140</a>.","short":"P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2019.","mla":"Swoboda, Paul, and Vladimir Kolmogorov. “Map Inference via Block-Coordinate Frank-Wolfe Algorithm.” <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>, vol. 2019–June, 11138–11147, IEEE, 2019, doi:<a href=\"https://doi.org/10.1109/CVPR.2019.01140\">10.1109/CVPR.2019.01140</a>.","ista":"Swoboda P, Kolmogorov V. 2019. Map inference via block-coordinate Frank-Wolfe algorithm. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition vol. 2019–June, 11138–11147.","ama":"Swoboda P, Kolmogorov V. Map inference via block-coordinate Frank-Wolfe algorithm. In: <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>. Vol 2019-June. IEEE; 2019. doi:<a href=\"https://doi.org/10.1109/CVPR.2019.01140\">10.1109/CVPR.2019.01140</a>","apa":"Swoboda, P., &#38; Kolmogorov, V. (2019). Map inference via block-coordinate Frank-Wolfe algorithm. In <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i> (Vol. 2019–June). Long Beach, CA, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2019.01140\">https://doi.org/10.1109/CVPR.2019.01140</a>","ieee":"P. Swoboda and V. Kolmogorov, “Map inference via block-coordinate Frank-Wolfe algorithm,” in <i>Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</i>, Long Beach, CA, United States, 2019, vol. 2019–June."},"month":"06","status":"public","isi":1,"arxiv":1,"external_id":{"isi":["000542649304076"],"arxiv":["1806.05049"]},"author":[{"id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul","first_name":"Paul","last_name":"Swoboda"},{"id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","full_name":"Kolmogorov, Vladimir","first_name":"Vladimir","last_name":"Kolmogorov"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","publication":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","type":"conference","date_updated":"2025-07-10T11:54:39Z","scopus_import":"1","article_processing_charge":"No","oa":1,"conference":{"start_date":"2019-06-15","name":"CVPR: Conference on Computer Vision and Pattern Recognition","location":"Long Beach, CA, United States","end_date":"2019-06-20"},"day":"01","year":"2019","title":"Map inference via block-coordinate Frank-Wolfe algorithm","volume":"2019-June","article_number":"11138-11147","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean decomposition. We show empirically that our method outperforms state-of-the-art Lagrangean decomposition based algorithms on some challenging Markov Random Field, multi-label discrete tomography and graph matching problems."}],"main_file_link":[{"url":"https://arxiv.org/abs/1806.05049","open_access":"1"}],"ec_funded":1,"doi":"10.1109/CVPR.2019.01140","publication_status":"published"},{"oa_version":"Preprint","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publication_status":"published","isi":1,"author":[{"first_name":"Stefan","last_name":"Haller","full_name":"Haller, Stefan"},{"first_name":"Paul","last_name":"Swoboda","id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul"},{"last_name":"Savchynskyy","first_name":"Bogdan","full_name":"Savchynskyy, Bogdan"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2004.06370"}],"external_id":{"arxiv":["2004.06370"],"isi":["000485488906082"]},"arxiv":1,"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"We consider the MAP-inference problem for graphical models,which is a valued constraint satisfaction problem defined onreal numbers with a natural summation operation. We proposea family of relaxations (different from the famous Sherali-Adams hierarchy), which naturally define lower bounds for itsoptimum. This family always contains a tight relaxation andwe give an algorithm able to find it and therefore, solve theinitial non-relaxed NP-hard problem.The relaxations we consider decompose the original probleminto two non-overlapping parts: an easy LP-tight part and adifficult one. For the latter part a combinatorial solver must beused. As we show in our experiments, in a number of applica-tions the second, difficult part constitutes only a small fractionof the whole problem. This property allows to significantlyreduce the computational time of the combinatorial solver andtherefore solve problems which were out of reach before."}],"conference":{"start_date":"2018-02-02","location":"New Orleans, LU, United States","name":"AAAI: Conference on Artificial Intelligence","end_date":"2018-02-07"},"day":"01","oa":1,"title":"Exact MAP-inference by confining combinatorial search with LP relaxation","year":"2018","status":"public","article_processing_charge":"No","month":"02","page":"6581-6588","department":[{"_id":"VlKo"}],"date_created":"2019-02-13T13:32:48Z","citation":{"mla":"Haller, Stefan, et al. “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation.” <i>Proceedings of the 32st AAAI Conference on Artificial Intelligence</i>, AAAI Press, 2018, pp. 6581–88.","short":"S. Haller, P. Swoboda, B. Savchynskyy, in:, Proceedings of the 32st AAAI Conference on Artificial Intelligence, AAAI Press, 2018, pp. 6581–6588.","ista":"Haller S, Swoboda P, Savchynskyy B. 2018. Exact MAP-inference by confining combinatorial search with LP relaxation. Proceedings of the 32st AAAI Conference on Artificial Intelligence. AAAI: Conference on Artificial Intelligence, 6581–6588.","chicago":"Haller, Stefan, Paul Swoboda, and Bogdan Savchynskyy. “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation.” In <i>Proceedings of the 32st AAAI Conference on Artificial Intelligence</i>, 6581–88. AAAI Press, 2018.","apa":"Haller, S., Swoboda, P., &#38; Savchynskyy, B. (2018). Exact MAP-inference by confining combinatorial search with LP relaxation. In <i>Proceedings of the 32st AAAI Conference on Artificial Intelligence</i> (pp. 6581–6588). New Orleans, LU, United States: AAAI Press.","ieee":"S. Haller, P. Swoboda, and B. Savchynskyy, “Exact MAP-inference by confining combinatorial search with LP relaxation,” in <i>Proceedings of the 32st AAAI Conference on Artificial Intelligence</i>, New Orleans, LU, United States, 2018, pp. 6581–6588.","ama":"Haller S, Swoboda P, Savchynskyy B. Exact MAP-inference by confining combinatorial search with LP relaxation. In: <i>Proceedings of the 32st AAAI Conference on Artificial Intelligence</i>. AAAI Press; 2018:6581-6588."},"publisher":"AAAI Press","_id":"5978","scopus_import":"1","date_updated":"2023-09-19T14:26:52Z","quality_controlled":"1","publication":"Proceedings of the 32st AAAI Conference on Artificial Intelligence","type":"conference","date_published":"2018-02-01T00:00:00Z"},{"oa":1,"day":"01","year":"2018","title":"Maximum persistency via iterative relaxed inference with graphical models","volume":40,"publist_id":"6992","language":[{"iso":"eng"}],"corr_author":"1","abstract":[{"lang":"eng","text":"We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision."}],"main_file_link":[{"url":"https://arxiv.org/abs/1508.07902","open_access":"1"}],"doi":"10.1109/TPAMI.2017.2730884","publication_status":"published","publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","type":"journal_article","date_updated":"2026-04-16T09:54:52Z","scopus_import":"1","article_processing_charge":"No","isi":1,"arxiv":1,"external_id":{"isi":["000434294800010"],"arxiv":["1508.07902"]},"author":[{"last_name":"Shekhovtsov","first_name":"Alexander","full_name":"Shekhovtsov, Alexander"},{"id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul","first_name":"Paul","last_name":"Swoboda"},{"first_name":"Bogdan","last_name":"Savchynskyy","full_name":"Savchynskyy, Bogdan"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","oa_version":"Preprint","quality_controlled":"1","date_published":"2018-07-01T00:00:00Z","_id":"703","intvolume":"        40","issue":"7","publication_identifier":{"issn":["0162-8828"]},"date_created":"2018-12-11T11:48:01Z","department":[{"_id":"VlKo"}],"publisher":"IEEE","citation":{"apa":"Shekhovtsov, A., Swoboda, P., &#38; Savchynskyy, B. (2018). Maximum persistency via iterative relaxed inference with graphical models. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">https://doi.org/10.1109/TPAMI.2017.2730884</a>","ieee":"A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018.","ama":"Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative relaxed inference with graphical models. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2018;40(7):1668-1682. doi:<a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">10.1109/TPAMI.2017.2730884</a>","chicago":"Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2018. <a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">https://doi.org/10.1109/TPAMI.2017.2730884</a>.","ista":"Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(7), 1668–1682.","short":"A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1668–1682.","mla":"Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:<a href=\"https://doi.org/10.1109/TPAMI.2017.2730884\">10.1109/TPAMI.2017.2730884</a>."},"month":"07","status":"public","page":"1668-1682"},{"publication_status":"published","doi":"10.1109/CVPR.2017.530","ec_funded":1,"file_date_updated":"2020-07-14T12:48:15Z","abstract":[{"lang":"eng","text":"We propose a dual decomposition and linear program relaxation of the NP-hard minimum cost multicut problem. Unlike other polyhedral relaxations of the multicut polytope, it is amenable to efficient optimization by message passing. Like other polyhedral relaxations, it can be tightened efficiently by cutting planes.  We define an algorithm that alternates between message passing and efficient separation of cycle- and odd-wheel inequalities. This algorithm is more efficient than state-of-the-art algorithms based on linear programming, including algorithms written in the framework of leading commercial software, as we show in experiments with large instances of the problem from applications in computer vision, biomedical image analysis and data mining."}],"corr_author":"1","publist_id":"6526","language":[{"iso":"eng"}],"year":"2017","volume":2017,"title":"A message passing algorithm for the minimum cost multicut problem","oa":1,"conference":{"start_date":"2017-07-21","location":"Honolulu, HA, United States","end_date":"2017-07-26","name":"CVPR: Computer Vision and Pattern Recognition"},"day":"01","article_processing_charge":"No","scopus_import":"1","ddc":["000"],"type":"conference","date_updated":"2024-11-04T13:52:34Z","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","oa_version":"Submitted Version","external_id":{"isi":["000418371405009"]},"author":[{"last_name":"Swoboda","first_name":"Paul","id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul"},{"first_name":"Bjoern","last_name":"Andres","full_name":"Andres, Bjoern"}],"isi":1,"file":[{"date_updated":"2020-07-14T12:48:15Z","file_id":"5849","content_type":"application/pdf","creator":"dernst","relation":"main_file","access_level":"open_access","date_created":"2019-01-18T12:52:46Z","file_size":883264,"checksum":"7e51dacefa693574581a32da3eff63dc","file_name":"Swoboda_A_Message_Passing_CVPR_2017_paper.pdf"}],"has_accepted_license":"1","page":"4990-4999","month":"07","status":"public","publisher":"IEEE","citation":{"chicago":"Swoboda, Paul, and Bjoern Andres. “A Message Passing Algorithm for the Minimum Cost Multicut Problem,” 2017:4990–99. IEEE, 2017. <a href=\"https://doi.org/10.1109/CVPR.2017.530\">https://doi.org/10.1109/CVPR.2017.530</a>.","short":"P. Swoboda, B. Andres, in:, IEEE, 2017, pp. 4990–4999.","mla":"Swoboda, Paul, and Bjoern Andres. <i>A Message Passing Algorithm for the Minimum Cost Multicut Problem</i>. Vol. 2017, IEEE, 2017, pp. 4990–99, doi:<a href=\"https://doi.org/10.1109/CVPR.2017.530\">10.1109/CVPR.2017.530</a>.","ista":"Swoboda P, Andres B. 2017. A message passing algorithm for the minimum cost multicut problem. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4990–4999.","ieee":"P. Swoboda and B. Andres, “A message passing algorithm for the minimum cost multicut problem,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4990–4999.","apa":"Swoboda, P., &#38; Andres, B. (2017). A message passing algorithm for the minimum cost multicut problem (Vol. 2017, pp. 4990–4999). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2017.530\">https://doi.org/10.1109/CVPR.2017.530</a>","ama":"Swoboda P, Andres B. A message passing algorithm for the minimum cost multicut problem. In: Vol 2017. IEEE; 2017:4990-4999. doi:<a href=\"https://doi.org/10.1109/CVPR.2017.530\">10.1109/CVPR.2017.530</a>"},"date_created":"2018-12-11T11:49:11Z","department":[{"_id":"VlKo"}],"_id":"915","intvolume":"      2017","publication_identifier":{"isbn":["978-153860457-1"]},"date_published":"2017-07-01T00:00:00Z","project":[{"grant_number":"616160","_id":"25FBA906-B435-11E9-9278-68D0E5697425","name":"Discrete Optimization in Computer Vision: Theory and Practice","call_identifier":"FP7"}],"quality_controlled":"1"},{"has_accepted_license":"1","file":[{"file_id":"5848","date_updated":"2020-07-14T12:48:15Z","content_type":"application/pdf","creator":"dernst","access_level":"open_access","relation":"main_file","date_created":"2019-01-18T12:49:38Z","file_name":"2017_CVPR_Swoboda2.pdf","checksum":"e38a2740daad1ea178465843b5072906","file_size":944332}],"isi":1,"author":[{"first_name":"Paul","last_name":"Swoboda","full_name":"Swoboda, Paul","id":"446560C6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Rother, Carsten","last_name":"Rother","first_name":"Carsten"},{"full_name":"Abu Alhaija, Carsten","first_name":"Carsten","last_name":"Abu Alhaija"},{"full_name":"Kainmueller, Dagmar","last_name":"Kainmueller","first_name":"Dagmar"},{"last_name":"Savchynskyy","first_name":"Bogdan","full_name":"Savchynskyy, Bogdan"}],"external_id":{"isi":["000418371407018"]},"oa_version":"Submitted Version","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","project":[{"grant_number":"616160","name":"Discrete Optimization in Computer Vision: Theory and Practice","_id":"25FBA906-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"quality_controlled":"1","date_published":"2017-01-01T00:00:00Z","publication_identifier":{"isbn":["978-153860457-1"]},"intvolume":"      2017","_id":"916","department":[{"_id":"VlKo"}],"date_created":"2018-12-11T11:49:11Z","citation":{"mla":"Swoboda, Paul, et al. <i>A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching</i>. Vol. 2017, IEEE, 2017, pp. 7062–71, doi:<a href=\"https://doi.org/10.1109/CVPR.2017.747\">10.1109/CVPR.2017.747</a>.","short":"P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, B. Savchynskyy, in:, IEEE, 2017, pp. 7062–7071.","ista":"Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. 2017. A study of lagrangean decompositions and dual ascent solvers for graph matching. CVPR: Computer Vision and Pattern Recognition vol. 2017, 7062–7071.","chicago":"Swoboda, Paul, Carsten Rother, Carsten Abu Alhaija, Dagmar Kainmueller, and Bogdan Savchynskyy. “A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching,” 2017:7062–71. IEEE, 2017. <a href=\"https://doi.org/10.1109/CVPR.2017.747\">https://doi.org/10.1109/CVPR.2017.747</a>.","apa":"Swoboda, P., Rother, C., Abu Alhaija, C., Kainmueller, D., &#38; Savchynskyy, B. (2017). A study of lagrangean decompositions and dual ascent solvers for graph matching (Vol. 2017, pp. 7062–7071). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2017.747\">https://doi.org/10.1109/CVPR.2017.747</a>","ieee":"P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, and B. Savchynskyy, “A study of lagrangean decompositions and dual ascent solvers for graph matching,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 7062–7071.","ama":"Swoboda P, Rother C, Abu Alhaija C, Kainmueller D, Savchynskyy B. A study of lagrangean decompositions and dual ascent solvers for graph matching. In: Vol 2017. IEEE; 2017:7062-7071. doi:<a href=\"https://doi.org/10.1109/CVPR.2017.747\">10.1109/CVPR.2017.747</a>"},"publisher":"IEEE","status":"public","month":"01","page":"7062-7071","conference":{"start_date":"2017-07-21","name":"CVPR: Computer Vision and Pattern Recognition","end_date":"2017-07-26","location":"Honolulu, HA, United States"},"day":"01","oa":1,"title":"A study of lagrangean decompositions and dual ascent solvers for graph matching","volume":2017,"year":"2017","language":[{"iso":"eng"}],"publist_id":"6525","corr_author":"1","abstract":[{"lang":"eng","text":"We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art anytime solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each."}],"file_date_updated":"2020-07-14T12:48:15Z","ec_funded":1,"doi":"10.1109/CVPR.2017.747","publication_status":"published","date_updated":"2024-11-04T13:52:34Z","type":"conference","ddc":["000"],"scopus_import":"1","article_processing_charge":"No"},{"language":[{"iso":"eng"}],"publist_id":"6524","corr_author":"1","abstract":[{"lang":"eng","text":"We  propose  a  general  dual  ascent  framework  for  Lagrangean decomposition of combinatorial problems.  Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers."}],"conference":{"start_date":"2017-07-21","location":"Honolulu, HA, United States","end_date":"2017-07-26","name":"CVPR: Computer Vision and Pattern Recognition"},"day":"01","oa":1,"volume":2017,"title":"A dual ascent framework for Lagrangean decomposition of combinatorial problems","year":"2017","doi":"10.1109/CVPR.2017.526","publication_status":"published","file_date_updated":"2020-07-14T12:48:15Z","ec_funded":1,"ddc":["000"],"scopus_import":"1","date_updated":"2024-11-04T13:52:34Z","type":"conference","article_processing_charge":"No","has_accepted_license":"1","file":[{"checksum":"72fd291046bd8e5717961bd68f6b6f03","file_size":898652,"file_name":"2017_CVPR_Swoboda.pdf","date_created":"2019-01-18T12:45:55Z","relation":"main_file","access_level":"open_access","creator":"dernst","content_type":"application/pdf","file_id":"5847","date_updated":"2020-07-14T12:48:15Z"}],"oa_version":"Submitted Version","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","isi":1,"author":[{"first_name":"Paul","last_name":"Swoboda","full_name":"Swoboda, Paul","id":"446560C6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Kuske, Jan","last_name":"Kuske","first_name":"Jan"},{"last_name":"Savchynskyy","first_name":"Bogdan","full_name":"Savchynskyy, Bogdan"}],"external_id":{"isi":["000418371405005"]},"intvolume":"      2017","publication_identifier":{"isbn":["978-153860457-1"]},"_id":"917","quality_controlled":"1","project":[{"call_identifier":"FP7","name":"Discrete Optimization in Computer Vision: Theory and Practice","_id":"25FBA906-B435-11E9-9278-68D0E5697425","grant_number":"616160"}],"date_published":"2017-07-01T00:00:00Z","status":"public","month":"07","page":"4950-4960","department":[{"_id":"VlKo"}],"date_created":"2018-12-11T11:49:11Z","citation":{"ieee":"P. Swoboda, J. Kuske, and B. Savchynskyy, “A dual ascent framework for Lagrangean decomposition of combinatorial problems,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 4950–4960.","apa":"Swoboda, P., Kuske, J., &#38; Savchynskyy, B. (2017). A dual ascent framework for Lagrangean decomposition of combinatorial problems (Vol. 2017, pp. 4950–4960). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2017.526\">https://doi.org/10.1109/CVPR.2017.526</a>","ama":"Swoboda P, Kuske J, Savchynskyy B. A dual ascent framework for Lagrangean decomposition of combinatorial problems. In: Vol 2017. IEEE; 2017:4950-4960. doi:<a href=\"https://doi.org/10.1109/CVPR.2017.526\">10.1109/CVPR.2017.526</a>","ista":"Swoboda P, Kuske J, Savchynskyy B. 2017. A dual ascent framework for Lagrangean decomposition of combinatorial problems. CVPR: Computer Vision and Pattern Recognition vol. 2017, 4950–4960.","short":"P. Swoboda, J. Kuske, B. Savchynskyy, in:, IEEE, 2017, pp. 4950–4960.","mla":"Swoboda, Paul, et al. <i>A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems</i>. Vol. 2017, IEEE, 2017, pp. 4950–60, doi:<a href=\"https://doi.org/10.1109/CVPR.2017.526\">10.1109/CVPR.2017.526</a>.","chicago":"Swoboda, Paul, Jan Kuske, and Bogdan Savchynskyy. “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems,” 2017:4950–60. IEEE, 2017. <a href=\"https://doi.org/10.1109/CVPR.2017.526\">https://doi.org/10.1109/CVPR.2017.526</a>."},"publisher":"IEEE"},{"oa_version":"None","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"full_name":"Trajkovska, Vera","last_name":"Trajkovska","first_name":"Vera"},{"last_name":"Swoboda","first_name":"Paul","id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul"},{"full_name":"Åström, Freddie","last_name":"Åström","first_name":"Freddie"},{"full_name":"Petra, Stefanie","first_name":"Stefanie","last_name":"Petra"}],"external_id":{"isi":["000432210900026"]},"isi":1,"page":"323 - 334","status":"public","month":"01","citation":{"ama":"Trajkovska V, Swoboda P, Åström F, Petra S. Graphical model parameter learning by inverse linear programming. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:323-334. doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">10.1007/978-3-319-58771-4_26</a>","ieee":"V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334.","apa":"Trajkovska, V., Swoboda, P., Åström, F., &#38; Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, &#38; A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">https://doi.org/10.1007/978-3-319-58771-4_26</a>","short":"V. Trajkovska, P. Swoboda, F. Åström, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 323–334.","mla":"Trajkovska, Vera, et al. <i>Graphical Model Parameter Learning by Inverse Linear Programming</i>. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 323–34, doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">10.1007/978-3-319-58771-4_26</a>.","ista":"Trajkovska V, Swoboda P, Åström F, Petra S. 2017. Graphical model parameter learning by inverse linear programming. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 323–334.","chicago":"Trajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefanie Petra. “Graphical Model Parameter Learning by Inverse Linear Programming.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:323–34. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">https://doi.org/10.1007/978-3-319-58771-4_26</a>."},"publisher":"Springer","department":[{"_id":"VlKo"}],"date_created":"2018-12-11T11:47:39Z","publication_identifier":{"isbn":["978-331958770-7"]},"intvolume":"     10302","_id":"641","date_published":"2017-01-01T00:00:00Z","quality_controlled":"1","publication_status":"published","doi":"10.1007/978-3-319-58771-4_26","abstract":[{"text":"We introduce two novel methods for learning parameters of graphical models for image labelling. The following two tasks underline both methods: (i) perturb model parameters based on given features and ground truth labelings, so as to exactly reproduce these labelings as optima of the local polytope relaxation of the labelling problem; (ii) train a predictor for the perturbed model parameters so that improved model parameters can be applied to the labelling of novel data. Our first method implements task (i) by inverse linear programming and task (ii) using a regressor e.g. a Gaussian process. Our second approach simultaneously solves tasks (i) and (ii) in a joint manner, while being restricted to linearly parameterised predictors. Experiments demonstrate the merits of both approaches.","lang":"eng"}],"language":[{"iso":"eng"}],"publist_id":"7147","title":"Graphical model parameter learning by inverse linear programming","volume":10302,"year":"2017","alternative_title":["LNCS"],"day":"01","conference":{"start_date":"2017-06-04","name":"SSVM: Scale Space and Variational Methods in Computer Vision","location":"Kolding, Denmark","end_date":"2017-06-08"},"editor":[{"full_name":"Lauze, François","first_name":"François","last_name":"Lauze"},{"full_name":"Dong, Yiqiu","last_name":"Dong","first_name":"Yiqiu"},{"first_name":"Anders","last_name":"Bjorholm Dahl","full_name":"Bjorholm Dahl, Anders"}],"article_processing_charge":"No","scopus_import":"1","date_updated":"2025-09-11T07:20:56Z","type":"conference"},{"oa_version":"Submitted Version","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","isi":1,"author":[{"first_name":"Jan","last_name":"Kuske","full_name":"Kuske, Jan"},{"first_name":"Paul","last_name":"Swoboda","id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul"},{"full_name":"Petra, Stefanie","last_name":"Petra","first_name":"Stefanie"}],"arxiv":1,"external_id":{"isi":["000432210900019"],"arxiv":["1703.03769"]},"intvolume":"     10302","publication_identifier":{"isbn":["978-331958770-7"]},"_id":"646","quality_controlled":"1","project":[{"call_identifier":"FP7","name":"Discrete Optimization in Computer Vision: Theory and Practice","_id":"25FBA906-B435-11E9-9278-68D0E5697425","grant_number":"616160"}],"date_published":"2017-06-01T00:00:00Z","status":"public","month":"06","page":"235 - 246","department":[{"_id":"VlKo"}],"date_created":"2018-12-11T11:47:41Z","citation":{"chicago":"Kuske, Jan, Paul Swoboda, and Stefanie Petra. “A Novel Convex Relaxation for Non Binary Discrete Tomography.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:235–46. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">https://doi.org/10.1007/978-3-319-58771-4_19</a>.","mla":"Kuske, Jan, et al. <i>A Novel Convex Relaxation for Non Binary Discrete Tomography</i>. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 235–46, doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">10.1007/978-3-319-58771-4_19</a>.","short":"J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 235–246.","ista":"Kuske J, Swoboda P, Petra S. 2017. A novel convex relaxation for non binary discrete tomography. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 235–246.","apa":"Kuske, J., Swoboda, P., &#38; Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, &#38; A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">https://doi.org/10.1007/978-3-319-58771-4_19</a>","ieee":"J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.","ama":"Kuske J, Swoboda P, Petra S. A novel convex relaxation for non binary discrete tomography. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:235-246. doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">10.1007/978-3-319-58771-4_19</a>"},"publisher":"Springer","language":[{"iso":"eng"}],"publist_id":"7132","abstract":[{"lang":"eng","text":"We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations."}],"day":"01","conference":{"start_date":"2017-06-04","name":"SSVM: Scale Space and Variational Methods in Computer Vision","location":"Kolding, Denmark","end_date":"2017-06-08"},"oa":1,"volume":10302,"title":"A novel convex relaxation for non binary discrete tomography","alternative_title":["LNCS"],"year":"2017","doi":"10.1007/978-3-319-58771-4_19","publication_status":"published","main_file_link":[{"url":"https://arxiv.org/abs/1703.03769","open_access":"1"}],"ec_funded":1,"scopus_import":"1","date_updated":"2025-09-11T07:16:00Z","type":"conference","editor":[{"first_name":"François","last_name":"Lauze","full_name":"Lauze, François"},{"full_name":"Dong, Yiqiu","first_name":"Yiqiu","last_name":"Dong"},{"last_name":"Bjorholm Dahl","first_name":"Anders","full_name":"Bjorholm Dahl, Anders"}],"article_processing_charge":"No"},{"file_date_updated":"2020-07-14T12:47:02Z","author":[{"first_name":"Paul","last_name":"Swoboda","id":"446560C6-F248-11E8-B48F-1D18A9856A87","full_name":"Swoboda, Paul"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.15479/AT:ISTA:46","oa_version":"Published Version","oa":1,"day":"20","year":"2016","title":"Synthetic discrete tomography problems","keyword":["discrete tomography"],"abstract":[{"lang":"eng","text":"Small synthetic discrete tomography problems.\r\nSizes are 32x32, 64z64 and 256x256.\r\nProjection angles are 2, 4, and 6.\r\nNumber of labels are 3 and 5."}],"file":[{"file_name":"IST-2016-46-v1+1_discrete_tomography_synthetic.zip","file_size":36058401,"checksum":"aa5a16a0dc888da7186fb8fc45e88439","date_created":"2018-12-12T13:05:19Z","access_level":"open_access","relation":"main_file","creator":"system","content_type":"application/zip","file_id":"5645","date_updated":"2020-07-14T12:47:02Z"}],"has_accepted_license":"1","department":[{"_id":"VlKo"}],"date_created":"2018-12-12T12:31:31Z","citation":{"ieee":"P. Swoboda, “Synthetic discrete tomography problems.” Institute of Science and Technology Austria, 2016.","apa":"Swoboda, P. (2016). Synthetic discrete tomography problems. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:46\">https://doi.org/10.15479/AT:ISTA:46</a>","ama":"Swoboda P. Synthetic discrete tomography problems. 2016. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:46\">10.15479/AT:ISTA:46</a>","short":"P. Swoboda, (2016).","mla":"Swoboda, Paul. <i>Synthetic Discrete Tomography Problems</i>. Institute of Science and Technology Austria, 2016, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:46\">10.15479/AT:ISTA:46</a>.","ista":"Swoboda P. 2016. Synthetic discrete tomography problems, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:46\">10.15479/AT:ISTA:46</a>.","chicago":"Swoboda, Paul. “Synthetic Discrete Tomography Problems.” Institute of Science and Technology Austria, 2016. <a href=\"https://doi.org/10.15479/AT:ISTA:46\">https://doi.org/10.15479/AT:ISTA:46</a>."},"publisher":"Institute of Science and Technology Austria","datarep_id":"46","month":"09","article_processing_charge":"No","status":"public","tmp":{"name":"Creative Commons Public Domain Dedication (CC0 1.0)","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","image":"/images/cc_0.png","short":"CC0 (1.0)"},"type":"research_data","date_updated":"2024-02-21T13:50:21Z","contributor":[{"first_name":"Jan","last_name":"Kuske","contributor_type":"data_collector"}],"license":"https://creativecommons.org/publicdomain/zero/1.0/","date_published":"2016-09-20T00:00:00Z","_id":"5557","ddc":["006"]}]