{"id":4051,"date":"2025-05-02T15:56:34","date_gmt":"2025-05-02T15:56:34","guid":{"rendered":"https:\/\/www.finden.co.uk\/?p=4051"},"modified":"2025-05-02T15:58:25","modified_gmt":"2025-05-02T15:58:25","slug":"latest-work-exploring-the-use-of-the-proxskip-algorithm","status":"publish","type":"post","link":"https:\/\/www.finden.co.uk\/zh\/news\/latest-work-exploring-the-use-of-the-proxskip-algorithm\/","title":{"rendered":"Latest work exploring the use of the ProxSkip algorithm\u00a0"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-4052 alignleft\" src=\"https:\/\/www.finden.co.uk\/wp-content\/uploads\/2025\/05\/comparison_proxskip.gif\" alt=\"Comparison Proxskip diagram\" width=\"2000\" height=\"1500\" \/><\/p>\n<p>We are excited to share our latest work exploring the use of the ProxSkip algorithm\u00a0as an efficient solution for accelerating iterative methods in imaging inverse problems. This project was led by our Senior Research Scientist <a href=\"https:\/\/epapoutsellis.github.io\/\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/epapoutsellis.github.io\/&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw1qn1dgNhexJ9V6rHpph3nS\">Evangelos Papoutsellis<\/a>, in collaboration with <a href=\"https:\/\/kostaspapafitsoros.weebly.com\/\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/kostaspapafitsoros.weebly.com\/&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw2QaGznCOhseiqpXF5helal\">Kostas Papafitsoros<\/a> (Queen Mary University) and <a href=\"https:\/\/maths4dl.ac.uk\/team-member\/dr-zeljko-kereta\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/maths4dl.ac.uk\/team-member\/dr-zeljko-kereta&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw1o0dfMxd-ybNhZy2XGD1sZ\">Zeljko Kereta<\/a> (University College London). By randomly skipping regularisation steps, ProxSkip significantly reduces computational time without compromising convergence. We also introduce a novel variant, <b>PDHGSkip<\/b>, which further enhances performance. Extensive numerical experiments demonstrate that these methods deliver faster computations while maintaining high-quality reconstructions.<\/p>\n<p>We acknowledge funding from from the\u00a0Analysis for Innovators (A4i) <i>Denoising of chemical imaging and tomography data\u00a0<\/i>project,<i>\u00a0<\/i>in collaboration with <a href=\"https:\/\/www.npl.co.uk\/\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.npl.co.uk\/&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw1HsQ_DIS5wJJmsbUhqEvNn\">Na<\/a><a href=\"https:\/\/www.npl.co.uk\/\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.npl.co.uk\/&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw1HsQ_DIS5wJJmsbUhqEvNn\">tional<\/a><a href=\"https:\/\/www.npl.co.uk\/\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.npl.co.uk\/&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw1HsQ_DIS5wJJmsbUhqEvNn\"> Physical Laboratory<\/a>\u00a0\u00a0which supported early development. As part of this effort, we also extended the stochastic optimisation framework in the <a href=\"https:\/\/github.com\/TomographicImaging\/CIL\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/github.com\/TomographicImaging\/CIL&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw2crbNE07CJcArozfmwffeB\">Core Imaging Library (CIL)<\/a>\u00a0to incorporate these new algorithms.\u00a0We are pleased to announce that this work has been accepted for presentation at the <a href=\"https:\/\/sites.google.com\/view\/ssvm-2025\/home-page\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/sites.google.com\/view\/ssvm-2025\/home-page&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw1PNq1yMh_ehPeX77hDCFei\">10th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM2025)<\/a>. For more information, we refer to the preprint version <a href=\"https:\/\/arxiv.org\/abs\/2411.00688\" target=\"_blank\" rel=\"noopener\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/arxiv.org\/abs\/2411.00688&amp;source=gmail&amp;ust=1746285967144000&amp;usg=AOvVaw02IKc0uKNcFqsQXUkSzqTJ\">https:\/\/arxiv.org\/abs\/2411.<wbr \/>00688<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>We are excited to share our latest work exploring the use of the ProxSkip algorithm\u00a0as an efficient solution for accelerating iterative methods in imaging inverse problems. This project was led by our Senior Research Scientist Evangelos Papoutsellis, in collaboration with Kostas Papafitsoros (Queen Mary University) and Zeljko Kereta (University College London). By randomly skipping regularisation [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[15],"tags":[37,149,148,21],"class_list":["post-4051","post","type-post","status-publish","format-standard","hentry","category-news","tag-chemical-imaging","tag-imaging","tag-proxskip","tag-tomography"],"_links":{"self":[{"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/posts\/4051","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/comments?post=4051"}],"version-history":[{"count":4,"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/posts\/4051\/revisions"}],"predecessor-version":[{"id":4056,"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/posts\/4051\/revisions\/4056"}],"wp:attachment":[{"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/media?parent=4051"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/categories?post=4051"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.finden.co.uk\/zh\/wp-json\/wp\/v2\/tags?post=4051"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}