The Fourier Transform is one of the most important linear transformations used in science and engineering. Cooley and Tukey's Fast Fourier Transform (FFT) from 1964 is a method for computing this transformation in time $O(n\log n)$. From a lower bound perspective, very little was known until recently. Ailon shows in 2013 an $\Omega(n\log n)$ for computing the normalized Fourier Transform assuming only unitary operations on two coordinates are allowed at each step, and no extra memory is allowed. In 2014, Ailon then improved the result to show that, in a $\kappa$-well conditioned computation, Fourier computation can be sped up by no more than $O(\kappa)$. The main conjecture is that [Ailon 2014] can be exponentially improved, in the sense that $\kappa$-well condition cannot admit $\omega(\log \kappa)$ speedup. #R##N#The main result here is that `algebraic' $\kappa$-well condition admits $O(\sqrt \kappa)$ speedup. The definition of algebraic condition number is obtained by formally viewing multiplication by constants, as performed by the algorithm, as multiplication by indeterminates, giving rise to computation of polynomial matrices. The algebraic condition number is related to the degree of these polynomials. Using the max modulus theorem from complex analysis, we show that algebraic condition number upper bounds standard condition number, and equals it in certain cases. Algebraic condition number is an interesting measure of numerical computation stability in its own right. Moreover, we believe that the approach of algebraic condition number has a good chance of establishing an algebraic version of the main conjecture.
['Nir Ailon']
Paraunitary Matrices, Entropy, Algebraic Condition Number and Fourier Computation