A Unified Implementation of Quasi-Monte Carlo Generators, Randomization Routines, and Fast Kernel Methods

Abstract: Quasi-random sequences, also called low-discrepancy sequences, have been extensively used as efficient experimental designs across many scientific disciplines. This article provides a unified description and software API for methods pertaining to low-discrepancy point sets. These methods include low discrepancy point set generators, randomization techniques, and fast kernel methods. Specifically, we provide generators for lattices, digital nets, and Halton point sets. Supported randomization techniques include random permutations / shifts, linear matrix scrambling, and nested uniform scrambling. Routines for working with higher-order digital nets and scramblings are also detailed. For kernel methods, we provide implementations of special shift-invariant and digitally-shift invariant kernels along with fast Gram matrix operations facilitated by the bit-reversed FFT, the bit-reversed IFFT, and the FWHT. A new digitally-shift-invariant kernel of higher-order smoothness is also derived. We also describe methods to quickly update the matrix-vector product or linear system solution after doubling the number of points in a lattice or digital net in natural order.