
Flows in Almost Linear Time via Adaptive Preconditioning
We present algorithms for solving a large class of flow and regression p...
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Faster pnorm minimizing flows, via smoothed qnorm problems
We present faster highaccuracy algorithms for computing ℓ_pnorm minimi...
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CinematicL1 Video Stabilization with a LogHomography Model
We present a method for stabilizing handheld video that simulates the ca...
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NearLinear Time Algorithms for Streett Objectives in Graphs and MDPs
The fundamental modelchecking problem, given as input a model and a spe...
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Bipartite Matching in Nearlylinear Time on Moderately Dense Graphs
We present an Õ(m+n^1.5)time randomized algorithm for maximum cardinali...
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Iterative Refinement for ℓ_pnorm Regression
We give improved algorithms for the ℓ_pregression problem, _xx_p such t...
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A Potential Reduction Inspired Algorithm for Exact Max Flow in Almost O(m^4/3) Time
We present an algorithm for computing st maximum flows in directed grap...
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Almostlineartime Weighted ℓ_pnorm Solvers in Slightly Dense Graphs via Sparsification
We give almostlineartime 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 multiplicativeweights based constantapproximation algorithm for mixedobjective flows or voltages, we show how to find (1+2^poly(log n)) approximations for weighted ℓ_pnorm minimizing flows or voltages in p(m^1+o(1) + n^4/3 + o(1)) time for p=ω(1), which is almostlinear for graphs that are slightly dense (m ≥ n^4/3 + o(1)).
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