Timeline for Solving a Weighted Basis Pursuit Denoising Problem (BPDN) with MATLAB / CVX
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Nov 14, 2021 at 7:08 | vote | accept | bla | ||
| Nov 14, 2021 at 6:52 | comment | added | Royi |
Yes, vX0 is the reference and vX is the solution. They are not close even in the case vC = 1 as the issue is with the sparse reconstruction. But the question is about solving the problem, not if the sparse model solution is perfect.
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| Nov 14, 2021 at 6:18 | comment | added | bla | maybe I dont understand from your code, the noiseless signal mA * vX0 and the noisy signal vY , but you solve for vX so should I compare vX to vX0 ? (because the values there are not close) | |
| Nov 14, 2021 at 6:10 | comment | added | Royi |
I removed the need for InitScript.m. vC is the vector of variances. Hence the noise added is by it. In order to create the inverse of the matrix $ C $ I just used the inverse of each value (As it is a diagonal matrix).
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| Nov 14, 2021 at 5:39 | history | edited | Royi | CC BY-SA 4.0 |
deleted 104 characters in body
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| Nov 14, 2021 at 5:35 | comment | added | bla |
I dont have InitScript.m is that important? is mCInv similar to 1./(sqrt(w)) in my example?
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| Nov 13, 2021 at 20:47 | history | edited | Peter K.♦ | CC BY-SA 4.0 |
Removing white space to stop vertical scrolling
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| Nov 13, 2021 at 19:08 | history | answered | Royi | CC BY-SA 4.0 |