{"id":1525,"date":"2020-03-27T12:50:26","date_gmt":"2020-03-27T11:50:26","guid":{"rendered":"http:\/\/blogs.gm.fh-koeln.de\/ciop\/?p=1525"},"modified":"2020-03-27T12:55:31","modified_gmt":"2020-03-27T11:55:31","slug":"sacobra-1-2-now-available-on-cran","status":"publish","type":"post","link":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/2020\/03\/27\/sacobra-1-2-now-available-on-cran\/","title":{"rendered":"SACOBRA 1.2 Now Available on CRAN"},"content":{"rendered":"

\"\"<\/a><\/p>\n

 <\/p>\n

We are happy to inform you that a new version of the SACOBRA package<\/a> is available on CRAN. SACOBRA is a self-adjusting surrogate assisted optimization framework designed for expensive black-box constrained problems.<\/p>\n

This version of SACOBRA can handle black-box equality constraints<\/strong>\u00a0 as described in this paper<\/a>. Also the online model selection algorithm<\/strong> described in an award-winning paper<\/a> is available to use in our recent release of SACOBRA. Additionally, the implementation of all 24 G-problems<\/strong> (the most commonly used test-suite for black-box constrained optimization problems in optimization community) is directly available in our package. If you have used SACOBRA 1.1 you might have experienced repeated warning messages coming from nloptr package, this issue is also resolved for the moment.<\/p>\n

 <\/p>\n

You can easily install SACOBRA by running the following line in your R command line:<\/p>\n

install.packages(\"SACOBRA\",dependencies=T)<\/p>\n

 <\/p>\n

If you have any interesting or challenging constrained optimization problems that you want to solve efficiently, apply SACOBRA and let us know about your experience.<\/p>\n","protected":false},"excerpt":{"rendered":"

  We are happy to inform you that a new version of the SACOBRA package is available on CRAN. SACOBRA is a self-adjusting surrogate assisted optimization framework designed for expensive black-box constrained problems. This version of SACOBRA can handle black-box equality constraints\u00a0 as described in this paper. Also the online model selection algorithm described in... <\/a><\/p>\n","protected":false},"author":41,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[134],"tags":[55632,10143,55631,55630,55634,10157,17602,55633],"class_list":["post-1525","post","type-post","status-publish","format-standard","hentry","category-allgemein","tag-black-box","tag-constraint-optimization","tag-cop","tag-cran","tag-g-problem-implementation","tag-g-problems","tag-sacobra","tag-surrogate"],"acf":[],"_links":{"self":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/posts\/1525","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/comments?post=1525"}],"version-history":[{"count":12,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/posts\/1525\/revisions"}],"predecessor-version":[{"id":1540,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/posts\/1525\/revisions\/1540"}],"wp:attachment":[{"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/media?parent=1525"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/categories?post=1525"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/wp-json\/wp\/v2\/tags?post=1525"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}