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Mar 15, 2018 at 20:28 vote accept Creator
Mar 15, 2018 at 13:11 comment added Federico Poloni @Suvrit Note that if you change norm the problem $\min_{X\in\mathcal{T}} \|A-X\|_F$ in the Frobenius norm has a trivial solution instead --- no need to use SDP.
S Mar 15, 2018 at 13:02 history suggested Rodrigo de Azevedo CC BY-SA 3.0
This could be "massaged" into a semidefinite program (SDP) or quadratic program (QP), both convex. Pedantic edit of the title.
Mar 15, 2018 at 11:36 review Suggested edits
S Mar 15, 2018 at 13:02
Mar 15, 2018 at 10:56 comment added Federico Poloni What do you mean exactly by "the L^2 norm between the eigenvectors of the Toeplitz matrix and eigenvectors of the matrix $A$"? Can you turn that into a formula? Keep in mind that the eigenvectors are not uniquely defined. So, for instance, one could claim that the solution to your problem is always the identity matrix $I$, which has the exact same eigenvectors as $A$, so the distance is 0. (That's a valid choice for the eigenvectors of $I$, right?)
S Mar 14, 2018 at 22:55 history suggested Rodrigo de Azevedo
Added tag.
Mar 14, 2018 at 21:46 review Suggested edits
S Mar 14, 2018 at 22:55
Mar 14, 2018 at 21:39 answer added Rodrigo de Azevedo timeline score: 7
S May 8, 2016 at 6:42 history suggested Amir Sagiv CC BY-SA 3.0
changed tags+ latex + english + title
May 8, 2016 at 6:27 review Suggested edits
S May 8, 2016 at 6:42
May 7, 2016 at 23:19 history edited Creator CC BY-SA 3.0
added additional matrices.
May 6, 2016 at 1:38 comment added Suvrit One way is to solve $\min \|A-X\|$ such that $X \in \mathcal{T}$, where $\mathcal{T}$ is the set of Toeplitz matrices (this is a linear structure, so this particular problem can be solved using an SDP solver).
May 5, 2016 at 21:46 history asked Creator CC BY-SA 3.0