{"id":11096,"date":"2013-12-15T12:27:11","date_gmt":"2013-12-15T10:27:11","guid":{"rendered":"http:\/\/hgpu.org\/?p=11096"},"modified":"2013-12-15T12:27:11","modified_gmt":"2013-12-15T10:27:11","slug":"a-distributed-approximation-algorithm-for-mixed-packing-covering-linear-programs","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11096","title":{"rendered":"A Distributed Approximation Algorithm for Mixed Packing-Covering Linear Programs"},"content":{"rendered":"<p>Mixed packing-covering linear programs capture a simple but expressive subclass of linear programs. They commonly arise as linear programming relaxations of a number important combinatorial problems, including various network design and generalized matching problems. In this paper, we propose an efficient distributed approximation algorithm for solving mixed packing-covering problems which requires a poly-logarithmic number of passes over the input. Our algorithm is well-suited for parallel processing on GPUs, in shared-memory architectures, or on small clusters of commodity nodes. We report results of a case study for generalized bipartite matching problems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mixed packing-covering linear programs capture a simple but expressive subclass of linear programs. They commonly arise as linear programming relaxations of a number important combinatorial problems, including various network design and generalized matching problems. In this paper, we propose an efficient distributed approximation algorithm for solving mixed packing-covering problems which requires a poly-logarithmic number of [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,20,1470],"class_list":["post-11096","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-titan"],"views":2068,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11096","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11096"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11096\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11096"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}