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Sci. Comput."],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>A collection of algorithms is described for numerically computing with smooth functions defined on the unit disk. Low rank approximations to functions in polar geometries are formed by synthesizing the disk analogue of the double Fourier sphere method with a structure-preserving variant of iterative Gaussian elimination that is shown to converge geometrically for certain analytic functions. This adaptive procedure is near-optimal in its sampling strategy, producing approximants that are stable for differentiation and facilitate the use of FFT-based algorithms in both variables. The low rank form of the approximants is especially useful for operations such as integration and differentiation, reducing them to essentially one-dimensional procedures, and it is also exploited to formulate a new fast disk Poisson solver that computes low rank approximations to solutions. 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