{"id":1063305,"date":"2024-07-30T12:53:34","date_gmt":"2024-07-30T19:53:34","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1063305"},"modified":"2024-07-30T12:53:34","modified_gmt":"2024-07-30T19:53:34","slug":"streaming-algorithms-for-connectivity-augmentation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/streaming-algorithms-for-connectivity-augmentation\/","title":{"rendered":"Streaming Algorithms for Connectivity Augmentation"},"content":{"rendered":"
We study the k<\/span><\/span><\/span><\/span>-connectivity augmentation problem (k<\/span><\/span><\/span><\/span>-CAP) in the single-pass streaming model. Given a (<\/span>k<\/span>\u2212<\/span>1<\/span>)<\/span><\/span><\/span><\/span>-edge connected graph G<\/span>=<\/span>(<\/span>V<\/span>,<\/span>E<\/span>)<\/span><\/span><\/span><\/span> that is stored in memory, and a stream of weighted edges L<\/span><\/span><\/span><\/span> with weights in {<\/span>0<\/span>,<\/span>1<\/span>,<\/span>\u2026<\/span>,<\/span>W<\/span>}<\/span><\/span><\/span><\/span>, the goal is to choose a minimum weight subset L<\/span>\u2032<\/span><\/span>\u2286<\/span>L<\/span><\/span><\/span><\/span> such that G<\/span>\u2032<\/span><\/span>=<\/span>(<\/span>V<\/span>,<\/span>E<\/span>\u222a<\/span>L<\/span>\u2032<\/span><\/span>)<\/span><\/span><\/span><\/span> is k<\/span><\/span><\/span><\/span>-edge connected. We give a (<\/span>2<\/span>+<\/span>\u03f5<\/span>)<\/span><\/span><\/span><\/span>-approximation algorithm for this problem which requires to store O<\/span>(<\/span>\u03f5^(<\/span>\u2212<\/span>1) <\/span><\/span><\/span><\/span>n <\/span>log <\/span><\/span>n<\/span>)<\/span><\/span><\/span><\/span> words. Moreover, we show our result is tight: Any algorithm with better than 2<\/span><\/span><\/span><\/span>-approximation for the problem requires \u03a9<\/span>(<\/span>n^<\/span>2<\/span><\/span>)<\/span><\/span><\/span><\/span> bits of space even when k<\/span>=<\/span>2<\/span><\/span><\/span><\/span>. This establishes a gap between the optimal approximation factor one can obtain in the streaming vs the offline setting for k<\/span><\/span><\/span><\/span>-CAP. We study the k-connectivity augmentation problem (k-CAP) in the single-pass streaming model. Given a (k\u22121)-edge connected graph G=(V,E) that is stored in memory, and a stream of weighted edges L with weights in {0,1,\u2026,W}, the goal is to choose a minimum weight subset L\u2032\u2286L such that G\u2032=(V,E\u222aL\u2032) is k-edge connected. We give a (2+\u03f5)-approximation algorithm […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1063305","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2024-7-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2402.10806","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Ce Jin","user_id":0,"rest_url":false},{"type":"text","value":"Michael Kapralov","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Sepideh Mahabadi","user_id":40780,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sepideh Mahabadi"},{"type":"text","value":"Ali Vakilian","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[437022],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1063305"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":6,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1063305\/revisions"}],"predecessor-version":[{"id":1063335,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1063305\/revisions\/1063335"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1063305"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=1063305"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=1063305"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1063305"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=1063305"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=1063305"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=1063305"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=1063305"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=1063305"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1063305"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1063305"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=1063305"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=1063305"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=1063305"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1063305"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=1063305"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nWe further consider a natural generalization to the fully streaming model where both E<\/span><\/span><\/span><\/span> and L<\/span><\/span><\/span><\/span> arrive in the stream in an arbitrary order. We show that this problem has a space lower bound that matches the best possible size of a spanner of the same approximation ratio. Following this, we give improved results for spanners on weighted graphs: We show a streaming algorithm that finds a (<\/span>2<\/span>t<\/span>\u2212<\/span>1<\/span>+<\/span>\u03f5<\/span>)<\/span><\/span><\/span><\/span>-approximate weighted spanner of size at most O<\/span>(<\/span>\u03f5^(<\/span>\u2212<\/span>1) <\/span><\/span><\/span><\/span>n^(<\/span>1<\/span>+<\/span>1<\/span>\/<\/span><\/span><\/span>t) <\/span><\/span><\/span><\/span>log <\/span><\/span>n<\/span>)<\/span><\/span><\/span><\/span> for integer t<\/span><\/span><\/span><\/span>, whereas the best prior streaming algorithm for spanner on weighted graphs had size depending on log <\/span><\/span>W<\/span><\/span><\/span><\/span>. Using our spanner result, we provide an optimal O<\/span>(<\/span>t<\/span>)<\/span><\/span><\/span><\/span>-approximation for k<\/span><\/span><\/span><\/span>-CAP in the fully streaming model with O<\/span>(<\/span>n<\/span>k<\/span>+<\/span>n^(<\/span>1<\/span>+<\/span>1<\/span>\/<\/span><\/span><\/span>t) <\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span> words of space.
\nFinally we apply our results to network design problems such as Steiner tree augmentation problem (STAP), k<\/span><\/span><\/span><\/span>-edge connected spanning subgraph (k<\/span><\/span><\/span><\/span>-ECSS), and the general Survivable Network Design problem (SNDP). In particular, we show a single-pass O<\/span>(<\/span>t <\/span>log<\/span><\/span>k<\/span>)<\/span><\/span><\/span><\/span>-approximation for SNDP using O<\/span>(<\/span>k <\/span>n^(<\/span>1<\/span>+<\/span>1<\/span>\/<\/span><\/span><\/span>t)<\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span> words of space, where k<\/span><\/span><\/span><\/span> is the maximum connectivity requirement<\/p>\n","protected":false},"excerpt":{"rendered":"