Almost-linear-time Weighted ℓ_p-norm Solvers in Slightly Dense Graphs via Sparsification

02/13/2021 ∙ by Deeksha Adil, et al. ∙ 0

We give almost-linear-time algorithms for constructing sparsifiers with npoly(log n) edges that approximately preserve weighted (ℓ^2_2 + ℓ^p_p) flow or voltage objectives on graphs. For flow objectives, this is the first sparsifier construction for such mixed objectives beyond unit ℓ_p weights, and is based on expander decompositions. For voltage objectives, we give the first sparsifier construction for these objectives, which we build using graph spanners and leverage score sampling. Together with the iterative refinement framework of [Adil et al, SODA 2019], and a new multiplicative-weights based constant-approximation algorithm for mixed-objective flows or voltages, we show how to find (1+2^-poly(log n)) approximations for weighted ℓ_p-norm minimizing flows or voltages in p(m^1+o(1) + n^4/3 + o(1)) time for p=ω(1), which is almost-linear for graphs that are slightly dense (m ≥ n^4/3 + o(1)).

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