# Computational practice in mathematical programming by M. L. Balinski, Eli Hellerman

By M. L. Balinski, Eli Hellerman

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Extra info for Computational practice in mathematical programming

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14. 13 means that the high-frequency content of the music has been removed, and the music then sounds flat. 15, where the frequency axis of the time-frequency plane is matched with the frequency domain that displays the Fourier transform of the data. Observe that since the noise content has larger amplitude than the music itself on the same frequency ranges, it shows up much more prominently. 16, we also show the DWT of the same music data. The rectangular structure indicates that only discrete values of the IWT are obtained, but they FIG.

It will be seen that this parameter is also used to slide the frequency window along the frequency axis. More precisely, let us consider the integral transform TIME-FREQUENCY LOCALIZATION FlG. 6. Chirp signal (with frequency increasing with time). FIG. 7. FIG. 8. 33 Flexible time window to zoom in. The time window narrows with decreasing values of the scale a. where a is the scale parameter. 8. we demonstrate how the width of the window function VK^Jr) is adjusted by changing the values of the scale a>0.

In this monograph, we will mainly consider scales that are integer powers of 2, and the relation we look for is the following. 1) is in L 2 . 3. 1) is called its corresponding two-scale sequence. 1) itself is called the twoscale relation ofs(t) is a scaling function, we recall that its Fourier transform (t) = (f)S(t) is the 47r-periodic extension of %[_„ ^ (to).