{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:47:35Z","timestamp":1732038455636},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Low-rank methods for semi-definite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-based or Schatten-norm penalties, which are difficult to implement in practice due to high computational efforts. In this paper, we propose Entropy-Penalized Semi-Definite Programming (EP-SDP), which provides a unified framework for a broad class of penalty functions used in practice to promote a low-rank solution. We show that EP-SDP problems admit an efficient numerical algorithm, having (almost) linear time complexity of the gradient computation; this makes it useful for many machine learning and optimization problems. We illustrate the practical efficiency of our approach on several combinatorial optimization and machine learning problems.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/157","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"1123-1129","source":"Crossref","is-referenced-by-count":5,"title":["Entropy-Penalized Semidefinite Programming"],"prefix":"10.24963","author":[{"given":"Mikhail","family":"Krechetov","sequence":"first","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Nobel Street 3, Moscow, Russia"}]},{"given":"Jakub","family":"Marecek","sequence":"additional","affiliation":[{"name":"IBM Research -- Ireland, Technology Campus Damastown, Dublin D15, Ireland"}]},{"given":"Yury","family":"Maximov","sequence":"additional","affiliation":[{"name":"Los Alamos National Laboratory, MS-B284, Los Alamos, NM 87545, USA"},{"name":"Skolkovo Institute of Science and Technology, Nobel Street 3, Moscow, Russia"}]},{"given":"Martin","family":"Takac","sequence":"additional","affiliation":[{"name":"Lehigh University, 200 West Packer Avenue, Bethlehem, PA 18015, USA"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:47:12Z","timestamp":1564285632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/157"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/157","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}