Timeline for Finding a similarities and differences of sent of matrices
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Aug 30, 2018 at 14:32 | comment | added | Mark L. Stone | Note, I answered the original question. The question has now been edited to bear no relation to the question I answered. | |
| Mar 23, 2018 at 21:08 | comment | added | Mark L. Stone | I selected X, call it Xinit, as you described. But you can make it whatever you want. In my answer I wrote "A corresponding Yinit_k is calculated as the requisite matrix inverse corresponding to Xinit for the kth term." More explicitly, that is $Yinit_k = (X^ HR_k Xinit + I)^{-1}$, which makes it satisfy the constraint $Yinit_k(X^ HR_k Xinit + I) = I$, and therefore be feasible with respect to it. | |
| Mar 23, 2018 at 20:53 | comment | added | Mark L. Stone | You can find a single X which optimizes over several $R_k$. You need to sum objective function terms, such as I showed, for each $R_k$. The YALMIP solution I showed will require a separate $Y_k$ matrix variable and matrix constraint for each $R_k$ as I mentioned. You specified a problem which is not so easy, so unless you have a very clever method, the solution may require a lot of computation. You don't necessarily have to solve to optimality if you are content with an approximate solution. You can get improvement in the objective function on each iteration once feasibiility is attained. | |
| Mar 23, 2018 at 16:22 | history | edited | Mark L. Stone | CC BY-SA 3.0 |
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| Mar 23, 2018 at 14:11 | history | edited | Mark L. Stone | CC BY-SA 3.0 |
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| Mar 23, 2018 at 14:03 | history | edited | Mark L. Stone | CC BY-SA 3.0 |
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| Mar 23, 2018 at 13:52 | history | answered | Mark L. Stone | CC BY-SA 3.0 |