{"action":"created","issue":{"url":"https://api.github.com/repos/ginkgo-project/ginkgo/issues/72","repository_url":"https://api.github.com/repos/ginkgo-project/ginkgo","labels_url":"https://api.github.com/repos/ginkgo-project/ginkgo/issues/72/labels{/name}","comments_url":"https://api.github.com/repos/ginkgo-project/ginkgo/issues/72/comments","events_url":"https://api.github.com/repos/ginkgo-project/ginkgo/issues/72/events","html_url":"https://github.com/ginkgo-project/ginkgo/issues/72","id":333227610,"node_id":"MDU6SXNzdWUzMzMyMjc2MTA=","number":72,"title":"Computing the matrix norm","user":{"login":"gflegar","id":1261711,"node_id":"MDQ6VXNlcjEyNjE3MTE=","avatar_url":"https://avatars1.githubusercontent.com/u/1261711?v=4","gravatar_id":"","url":"https://api.github.com/users/gflegar","html_url":"https://github.com/gflegar","followers_url":"https://api.github.com/users/gflegar/followers","following_url":"https://api.github.com/users/gflegar/following{/other_user}","gists_url":"https://api.github.com/users/gflegar/gists{/gist_id}","starred_url":"https://api.github.com/users/gflegar/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gflegar/subscriptions","organizations_url":"https://api.github.com/users/gflegar/orgs","repos_url":"https://api.github.com/users/gflegar/repos","events_url":"https://api.github.com/users/gflegar/events{/privacy}","received_events_url":"https://api.github.com/users/gflegar/received_events","type":"User","site_admin":false},"labels":[{"id":801859198,"node_id":"MDU6TGFiZWw4MDE4NTkxOTg=","url":"https://api.github.com/repos/ginkgo-project/ginkgo/labels/Enhancement","name":"Enhancement","color":"D1D100","default":false},{"id":801859200,"node_id":"MDU6TGFiZWw4MDE4NTkyMDA=","url":"https://api.github.com/repos/ginkgo-project/ginkgo/labels/Good%20first%20issue","name":"Good first issue","color":"7057ff","default":false}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2018-06-18T11:16:38Z","updated_at":"2018-10-02T09:09:20Z","closed_at":null,"author_association":"MEMBER","body":"Sometimes it is beneficial to have a method that computes the norm of a matrix (or liner operator). (Currently we only have a function which computes the column-wise dot product of two dense matrices - which is enough for use in Krylov solvers, but not necessarily in all scenarios).\r\n\r\nThere are a few questions though on how we implement this:\r\n\r\n1.  There are multiple interesting norms (1, Frobenious, infinity, etc), do we want to support all of them? And how do we design the interface for that, multiple functions, one templated function, or one function with an extra parameter?\r\n2.   For which kinds of linear operators does it makes sense to implement the norm in the first place? Probably not for solvers. For all of the matrices it is not extremely difficult (depending on the norm). For some preconditioners (e.g block-Jacobi) it is easy, but for others it's more involved (e.g. ILU-type). Do we add it as a virtual method to LinOp, do we create a new interface which is inherited by all operators implementing it (e.g. like we have for `transpose`), or do we just put that as non-virtual members of the concrete linear operators implementing those?"},"comment":{"url":"https://api.github.com/repos/ginkgo-project/ginkgo/issues/comments/426203534","html_url":"https://github.com/ginkgo-project/ginkgo/issues/72#issuecomment-426203534","issue_url":"https://api.github.com/repos/ginkgo-project/ginkgo/issues/72","id":426203534,"node_id":"MDEyOklzc3VlQ29tbWVudDQyNjIwMzUzNA==","user":{"login":"gflegar","id":1261711,"node_id":"MDQ6VXNlcjEyNjE3MTE=","avatar_url":"https://avatars1.githubusercontent.com/u/1261711?v=4","gravatar_id":"","url":"https://api.github.com/users/gflegar","html_url":"https://github.com/gflegar","followers_url":"https://api.github.com/users/gflegar/followers","following_url":"https://api.github.com/users/gflegar/following{/other_user}","gists_url":"https://api.github.com/users/gflegar/gists{/gist_id}","starred_url":"https://api.github.com/users/gflegar/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gflegar/subscriptions","organizations_url":"https://api.github.com/users/gflegar/orgs","repos_url":"https://api.github.com/users/gflegar/repos","events_url":"https://api.github.com/users/gflegar/events{/privacy}","received_events_url":"https://api.github.com/users/gflegar/received_events","type":"User","site_admin":false},"created_at":"2018-10-02T09:09:20Z","updated_at":"2018-10-02T09:09:20Z","author_association":"MEMBER","body":"@tcojean #141 does something different - it computes (or at least should compute) the norm of each column of the matrix, so the result is a row-vector of norms. This is a feature request for computing the norm of the entire matrix, which should produce one value for the entire matrix."}}
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