{"id":4604,"date":"2016-09-06T11:40:14","date_gmt":"2016-09-06T10:40:14","guid":{"rendered":"http:\/\/smart--grid.net\/?page_id=4604"},"modified":"2022-12-03T23:00:11","modified_gmt":"2022-12-03T22:00:11","slug":"cutting-plane","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/es\/optimizacion-combinatoria\/plano-de-corte\/","title":{"rendered":"Plano de corte"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"4604\" class=\"elementor elementor-4604\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2b4a824 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2b4a824\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b5028c2\" data-id=\"b5028c2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1288049 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"1288049\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/es\/optimizacion-combinatoria\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Optimizaci\u00f3n combinatoria<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b4906c8\" data-id=\"b4906c8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1564933 elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"1564933\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/complex-systems-ai.com\/es\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Pagina de inicio<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-529475a\" data-id=\"529475a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d173e0e elementor-align-justify elementor-widget elementor-widget-button\" data-id=\"d173e0e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/fr.wikipedia.org\/wiki\/M%C3%A9thode_des_plans_s%C3%A9cants\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Wiki<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-14fcce5e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"14fcce5e\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-43bbe673\" data-id=\"43bbe673\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3b9e9f0 elementor-widget elementor-widget-text-editor\" data-id=\"3b9e9f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contenido<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Tabla de contenido alternativo\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Palanca<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/complex-systems-ai.com\/es\/optimizacion-combinatoria\/plano-de-corte\/#Methode-de-coupes-planes-cutting-plane\" >M\u00e9todo de plano de corte<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Methode-de-coupes-planes-cutting-plane\"><\/span>M\u00e9todo de plano de corte<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>El m\u00e9todo del plano de corte fue desarrollado por Schrijver, est\u00e1 destinado a resolver problemas de optimizaci\u00f3n combinatoria (POC) que se formulan en forma de un programa lineal (PL):<\/p>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" class=\"alignnone wp-image-4706 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/09\/cuttingplane.png\" alt=\"plano de corte\" width=\"246\" height=\"45\" title=\"\"><\/figure>\n<\/div>\n\n<p>En el caso de que el POC sea de gran tama\u00f1o para representarlo expl\u00edcitamente en memoria o para que quepa en un solver de <a href=\"https:\/\/complex-systems-ai.com\/es\/programacion-lineal\/\">programaci\u00f3n lineal<\/a>, utilizamos una t\u00e9cnica que consiste en eliminar parte de estas restricciones y resolver el problema relajado (POCR). La soluci\u00f3n \u00f3ptima de (PL) est\u00e1 contenida en el conjunto de soluciones factibles de esta relajaci\u00f3n. por un problema de <a href=\"https:\/\/complex-systems-ai.com\/es\/teoria-del-lenguaje\/minimizacion-dun-afd\/\">minimizaci\u00f3n<\/a> la soluci\u00f3n \u00f3ptima del problema (POCR) es menor o igual a la soluci\u00f3n \u00f3ptima dada por (POC)<\/p>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><img decoding=\"async\" class=\"alignnone wp-image-4717 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/09\/cuttingplane2.png\" alt=\"plano de corte\" width=\"387\" height=\"28\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/09\/cuttingplane2.png 387w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2016\/09\/cuttingplane2-300x22.png 300w\" sizes=\"(max-width: 387px) 100vw, 387px\" \/><\/figure>\n<\/div>\n\n<p>Este m\u00e9todo consiste en resolver un problema relajado y en agregar restricciones iterativamente al problema inicial. Definimos una restricci\u00f3n para el problema de minimizaci\u00f3n por la pareja (s, s<sub>0<\/sub>) donde s est\u00e1 en R<sup>no<\/sup> ys<sub>0<\/sub> en R, se dice que esta restricci\u00f3n es violada por la soluci\u00f3n actual x (bar) si para todos:<br \/><br \/>Estas restricciones se denominan entonces secciones planas. Se detiene el algoritmo cuando no hay m\u00e1s restricciones violadas por la soluci\u00f3n actual, se obtiene as\u00ed una soluci\u00f3n \u00f3ptima para el problema inicial.<\/p>\n\n<p>El m\u00e9todo de cortes planos no es muy eficiente pero su rendimiento mejora cuando se combina con el m\u00e9todo <a href=\"https:\/\/complex-systems-ai.com\/es\/optimizacion-combinatoria\/ramificar-y-enlazar\/\">Rama y atado<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Optimizaci\u00f3n combinatoria P\u00e1gina de inicio de Wiki M\u00e9todo del plano de corte El m\u00e9todo del plano de corte fue desarrollado por Schrijver, est\u00e1 destinado a ... <\/p>","protected":false},"author":1,"featured_media":0,"parent":1770,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-4604","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/4604","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/comments?post=4604"}],"version-history":[{"count":4,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/4604\/revisions"}],"predecessor-version":[{"id":17897,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/4604\/revisions\/17897"}],"up":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/pages\/1770"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/es\/wp-json\/wp\/v2\/media?parent=4604"}],"curies":[{"name":"gracias","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}