{"action":"opened","issue":{"url":"https://api.github.com/repos/BBN-Q/QuantumTomography.jl/issues/5","repository_url":"https://api.github.com/repos/BBN-Q/QuantumTomography.jl","labels_url":"https://api.github.com/repos/BBN-Q/QuantumTomography.jl/issues/5/labels{/name}","comments_url":"https://api.github.com/repos/BBN-Q/QuantumTomography.jl/issues/5/comments","events_url":"https://api.github.com/repos/BBN-Q/QuantumTomography.jl/issues/5/events","html_url":"https://github.com/BBN-Q/QuantumTomography.jl/issues/5","id":187536226,"number":5,"title":"Exponential cone convex programs fail to converge","user":{"login":"marcusps","id":232049,"avatar_url":"https://avatars.githubusercontent.com/u/232049?v=3","gravatar_id":"","url":"https://api.github.com/users/marcusps","html_url":"https://github.com/marcusps","followers_url":"https://api.github.com/users/marcusps/followers","following_url":"https://api.github.com/users/marcusps/following{/other_user}","gists_url":"https://api.github.com/users/marcusps/gists{/gist_id}","starred_url":"https://api.github.com/users/marcusps/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/marcusps/subscriptions","organizations_url":"https://api.github.com/users/marcusps/orgs","repos_url":"https://api.github.com/users/marcusps/repos","events_url":"https://api.github.com/users/marcusps/events{/privacy}","received_events_url":"https://api.github.com/users/marcusps/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-11-06T01:55:25Z","updated_at":"2016-11-06T01:55:25Z","closed_at":null,"body":"This prevents ML tomography from using a convex program formulation (thus the use of the iterative R \\rho R algorithm) or the use of hedging.  Moreover, SCS is the only solver I am aware of that can solve mixed semidefinite and exponential cone programs, and SCS is currently having issues even with pure SDP formulations of tomography.\r\n\r\nPerhaps an alternative is to use projected gradient searches -- should be flexible enough to handle maximum likelihood, hedging, maximum entropy, etc."}}
{"actor":{"display_login":"marcusps"}}