{"action":"created","comment":{"url":"https://api.github.com/repos/openturns/openturns/pulls/comments/177697909","pull_request_review_id":107613739,"id":177697909,"diff_hunk":"@@ -355,7 +355,25 @@ optimizeParameters : bool\n Parameters\n ----------\n bounds : :class:`~openturns.Interval`\n-    Bounds for covariance model parameter optimization.\"\n+    Bounds for covariance model parameter optimization.\n+\n+Notes\n+-----\n+Parameters involved by this method are:\n+\n+ - Scale parameters,\n+ - Amplitude parameters if output dimension is greater than one or\n+   analytical sigma disabled,\n+ - Additional parameters.\n+\n+Default bounds in the library are :math:`[100]^d` with `d` the size\n+of optimization parameters. However it is recommended to set the","path":"python/src/GeneralLinearModelAlgorithm_doc.i.in","position":17,"original_position":17,"commit_id":"d151baacce2788f90eb31b13c6e560e857381b7d","original_commit_id":"964449119ee97e276bee0aa3660bb5959b3515b7","user":{"login":"regislebrun","id":8220933,"avatar_url":"https://avatars0.githubusercontent.com/u/8220933?v=4","gravatar_id":"","url":"https://api.github.com/users/regislebrun","html_url":"https://github.com/regislebrun","followers_url":"https://api.github.com/users/regislebrun/followers","following_url":"https://api.github.com/users/regislebrun/following{/other_user}","gists_url":"https://api.github.com/users/regislebrun/gists{/gist_id}","starred_url":"https://api.github.com/users/regislebrun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/regislebrun/subscriptions","organizations_url":"https://api.github.com/users/regislebrun/orgs","repos_url":"https://api.github.com/users/regislebrun/repos","events_url":"https://api.github.com/users/regislebrun/events{/privacy}","received_events_url":"https://api.github.com/users/regislebrun/received_events","type":"User","site_admin":false},"body":"If a strategy is recommended, shouldn't it be the default behavior?","created_at":"2018-03-28T09:58:45Z","updated_at":"2018-03-28T10:02:25Z","html_url":"https://github.com/openturns/openturns/pull/728#discussion_r177697909","pull_request_url":"https://api.github.com/repos/openturns/openturns/pulls/728","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/openturns/openturns/pulls/comments/177697909"},"html":{"href":"https://github.com/openturns/openturns/pull/728#discussion_r177697909"},"pull_request":{"href":"https://api.github.com/repos/openturns/openturns/pulls/728"}}},"pull_request":{"url":"https://api.github.com/repos/openturns/openturns/pulls/728","id":177725123,"html_url":"https://github.com/openturns/openturns/pull/728","diff_url":"https://github.com/openturns/openturns/pull/728.diff","patch_url":"https://github.com/openturns/openturns/pull/728.patch","issue_url":"https://api.github.com/repos/openturns/openturns/issues/728","number":728,"state":"open","locked":false,"title":"Kriging optimization enhancements","user":{"login":"sofianehaddad","id":1433220,"avatar_url":"https://avatars1.githubusercontent.com/u/1433220?v=4","gravatar_id":"","url":"https://api.github.com/users/sofianehaddad","html_url":"https://github.com/sofianehaddad","followers_url":"https://api.github.com/users/sofianehaddad/followers","following_url":"https://api.github.com/users/sofianehaddad/following{/other_user}","gists_url":"https://api.github.com/users/sofianehaddad/gists{/gist_id}","starred_url":"https://api.github.com/users/sofianehaddad/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sofianehaddad/subscriptions","organizations_url":"https://api.github.com/users/sofianehaddad/orgs","repos_url":"https://api.github.com/users/sofianehaddad/repos","events_url":"https://api.github.com/users/sofianehaddad/events{/privacy}","received_events_url":"https://api.github.com/users/sofianehaddad/received_events","type":"User","site_admin":false},"body":"In #615 we set normalization of optimization bounds in `KrigingAlgorithm` & `GeneralLinearModelAlgorithm` if input is normalized.\r\n\r\nIt seems to be an interesting idea as `scale` bounds should match the input data range. However the inputTransformation_(bounds) might be negative and the optimization algorithm fails.  (see http://trac.openturns.org/ticket/942 for example)\r\n\r\nThus the **normalization** of bounds is **removed**.\r\n\r\nUser should be pay attention to the used bounds. We warn user when using default optimization bounds in GLM with normalized input sample and we add recommendation on `optimal` bounds he could 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