Timeline for $\arg\max$ in the dual norm of the nuclear norm
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 10, 2020 at 0:14 | vote | accept | Santiago Armstrong | ||
| May 3, 2020 at 13:31 | answer | added | alesia | timeline score: 5 | |
| S May 3, 2020 at 12:41 | history | suggested | Rodrigo de Azevedo | CC BY-SA 4.0 |
Added higher order tag. Minor improvements for the sake of readability.
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| May 3, 2020 at 7:35 | review | Suggested edits | |||
| S May 3, 2020 at 12:41 | |||||
| May 3, 2020 at 4:27 | comment | added | DSM | import cvxpy as cvx <newline> import numpy as np <newline> N = 5 <newline> X = cvx.Variable((N,N)) <newline> M = np.random.randn(N,N) <newline> prob = cvx.Problem(cvx.Maximize(cvx.trace((M.T)@X)), [cvx.normNuc(X)<=1]) <newline> prob.solve() <newline> print(prob.status) <newline> print(prob.value) <newline> print(X.value) | |
| May 3, 2020 at 3:30 | comment | added | Santiago Armstrong | @DSM both actually. I am comfortable with python but I haven't used that library before. Is this is easy to solve using CVXPY? | |
| May 3, 2020 at 2:39 | comment | added | DSM | Solving the problem with CVXPY, as it is a convex program, should help. Are you looking for a more theoretical treatment of the problem? | |
| May 3, 2020 at 2:07 | review | First posts | |||
| May 3, 2020 at 2:08 | |||||
| May 3, 2020 at 2:00 | history | asked | Santiago Armstrong | CC BY-SA 4.0 |