Ozzy

Joined 25 March 2015
26 bytes added ,  26 March 2015
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<math> y \approx f(x)</math>
<math> y \approx f(x)</math>


It is convienient to have this version of the algorithm for problem where obtaining the transformation matrix is difficult to compute or affects performance (think fft). The algorithm is expected to give good results for linear functions. For non-linear the chances are still there.
It is convienient to have this version of the algorithm for problem where obtaining the transformation matrix is difficult to compute or affects performance (think fft). The algorithm is expected to give good results for linear functions. For not-too-complicated non-linear cases the chances are still there.


Additional  work will be put to provide some wrapper functions to allow the user quickly use MEM in their problem. This includes function for 1D and image deconvolutions, time series components analysis, power spectral estimation and other applications I will be able to find in Matlab or other computational software.
Additional  work will be put to provide some wrapper functions to allow the user quickly use MEM in their problem. This includes function for 1D and image deconvolutions, time series components analysis, power spectral estimation and other applications I will be able to find in Matlab or other computational software.
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