{"action":"opened","issue":{"url":"https://api.github.com/repos/cvxgrp/cvxpy/issues/216","labels_url":"https://api.github.com/repos/cvxgrp/cvxpy/issues/216/labels{/name}","comments_url":"https://api.github.com/repos/cvxgrp/cvxpy/issues/216/comments","events_url":"https://api.github.com/repos/cvxgrp/cvxpy/issues/216/events","html_url":"https://github.com/cvxgrp/cvxpy/issues/216","id":101336461,"number":216,"title":"ECOS infeasible for small LP","user":{"login":"ajfriend","id":984039,"avatar_url":"https://avatars.githubusercontent.com/u/984039?v=3","gravatar_id":"","url":"https://api.github.com/users/ajfriend","html_url":"https://github.com/ajfriend","followers_url":"https://api.github.com/users/ajfriend/followers","following_url":"https://api.github.com/users/ajfriend/following{/other_user}","gists_url":"https://api.github.com/users/ajfriend/gists{/gist_id}","starred_url":"https://api.github.com/users/ajfriend/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ajfriend/subscriptions","organizations_url":"https://api.github.com/users/ajfriend/orgs","repos_url":"https://api.github.com/users/ajfriend/repos","events_url":"https://api.github.com/users/ajfriend/events{/privacy}","received_events_url":"https://api.github.com/users/ajfriend/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-08-17T04:23:00Z","updated_at":"2015-08-17T04:23:00Z","closed_at":null,"body":"I made a small diet problem feasibility LP for a class, and ECOS seems to fail, while SCS and CVXOPT handle it fine.\r\n\r\nThe data:\r\n```\r\nfrom cvxpy import *\r\nimport numpy as np\r\nh = np.array([.8, .4, .5])\r\nc = np.array([0, .3, 2.0])\r\nr = np.array([1, 2.1, 1.7])\r\np = np.array([1, .25])\r\n```\r\n\r\nThe minimization version\r\n```\r\nx = Variable(2)\r\nobj = Minimize(x.T*p)\r\nconstr = [\r\n          h*x[0] + c*x[1] >= 50*r,\r\n          x >= 0]\r\n\r\nprob = Problem(obj, constr)\r\nprob.solve(solver='ECOS')\r\nprint prob.value\r\n```\r\ngives an optimal value of 129.166666672. If I write a feasibility version of the problem:\r\n```\r\nx = Variable(2)\r\nobj = Minimize(0)\r\nconstr = [x.T*p <= 130,\r\n          h*x[0] + c*x[1] >= 50*r,\r\n          x >= 0]\r\n\r\nprob = Problem(obj, constr)\r\nprob.solve(solver='ECOS')\r\nprint prob.value\r\n```\r\nECOS incorrectly determines the problem to be infeasible, which is weird, since it just solved the minimization version of the problem without any trouble. SCS and CVXOPT don't have any trouble with either version.\r\n\r\nAny idea what's going on?"}}