{"action":"created","issue":{"url":"https://api.github.com/repos/oyamad/theory16/issues/4","repository_url":"https://api.github.com/repos/oyamad/theory16","labels_url":"https://api.github.com/repos/oyamad/theory16/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/oyamad/theory16/issues/4/comments","events_url":"https://api.github.com/repos/oyamad/theory16/issues/4/events","html_url":"https://github.com/oyamad/theory16/issues/4","id":186242293,"number":4,"title":"Dynamic Programming: Comparison of three methods","user":{"login":"shizejin","id":22466278,"avatar_url":"https://avatars.githubusercontent.com/u/22466278?v=3","gravatar_id":"","url":"https://api.github.com/users/shizejin","html_url":"https://github.com/shizejin","followers_url":"https://api.github.com/users/shizejin/followers","following_url":"https://api.github.com/users/shizejin/following{/other_user}","gists_url":"https://api.github.com/users/shizejin/gists{/gist_id}","starred_url":"https://api.github.com/users/shizejin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/shizejin/subscriptions","organizations_url":"https://api.github.com/users/shizejin/orgs","repos_url":"https://api.github.com/users/shizejin/repos","events_url":"https://api.github.com/users/shizejin/events{/privacy}","received_events_url":"https://api.github.com/users/shizejin/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2016-10-31T09:47:58Z","updated_at":"2016-10-31T14:28:14Z","closed_at":null,"body":"Presentation in 1/11 will be about comparing three methods for numerically solving dynamic programming problems, which are Value Function Iteration, Policy Function Iteration, and Endogenous Grid Points.  \r\nI will follow the lecture notes by [ZICE 2014](https://github.com/davidrpugh/zice-2014/blob/master/endogenous-gridpoint-methods/iskhakov_egm_zice2014.pdf). The specific problem  to be solved is from [A Simple Optimal Growth Model](http://lectures.quantecon.org/py/optgrowth.html#id15) in [Quantitive Economics](http://lectures.quantecon.org). I use the same code in Value Function Iteration, and then extend it to Policy Iteration and Endogenous Grid Points."},"comment":{"url":"https://api.github.com/repos/oyamad/theory16/issues/comments/257308133","html_url":"https://github.com/oyamad/theory16/issues/4#issuecomment-257308133","issue_url":"https://api.github.com/repos/oyamad/theory16/issues/4","id":257308133,"user":{"login":"oyamad","id":7353083,"avatar_url":"https://avatars.githubusercontent.com/u/7353083?v=3","gravatar_id":"","url":"https://api.github.com/users/oyamad","html_url":"https://github.com/oyamad","followers_url":"https://api.github.com/users/oyamad/followers","following_url":"https://api.github.com/users/oyamad/following{/other_user}","gists_url":"https://api.github.com/users/oyamad/gists{/gist_id}","starred_url":"https://api.github.com/users/oyamad/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/oyamad/subscriptions","organizations_url":"https://api.github.com/users/oyamad/orgs","repos_url":"https://api.github.com/users/oyamad/repos","events_url":"https://api.github.com/users/oyamad/events{/privacy}","received_events_url":"https://api.github.com/users/oyamad/received_events","type":"User","site_admin":false},"created_at":"2016-10-31T14:28:14Z","updated_at":"2016-10-31T14:28:14Z","body":"No.\r\nThe optimal value function is a fixed point of the Bellman operator. Any fixed point solver can in theory find an (approximate) optimal value function. So why not use your own.\r\nFor example, you can just replace `qe.compute_fixed_point` in cell `[6]` with your routine."}}
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