Faster p-norm minimizing flows, via smoothed q-norm problems
Deeksha Adil, Sushant Sachdeva

TL;DR
This paper introduces faster algorithms for computing high-accuracy $ ext{ell}_p$-norm minimizing flows on graphs, achieving improved running times and extending applicability to a broader range of $p$ values.
Contribution
It presents novel algorithms that significantly improve the computational efficiency for $ ext{ell}_p$-norm flows and regression, utilizing interreducibility of smoothed $ ext{ell}_p$-norm problems.
Findings
Achieves $O(m^{1.24})$ time for $2 extless p extless m^{o(1)}$ flows.
Provides a $(1+ ext{delta})$-approximate maximum flow algorithm for certain $p$.
First high-accuracy $ ext{ell}_p$-norm flow algorithm with runtime $o(m^{1.5})$ for large $p$.
Abstract
We present faster high-accuracy algorithms for computing -norm minimizing flows. On a graph with edges, our algorithm can compute a -approximate unweighted -norm minimizing flow with operations, for any giving the best bound for all Combined with the algorithm from the work of Adil et al. (SODA '19), we can now compute such flows for any in time at most In comparison, the previous best running time was for large constant For our algorithm computes a -approximate maximum flow on undirected graphs using operations, matching the current best bound, albeit only for unit-capacity graphs. We also give an algorithm for solving general -norm regression problems for…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Stochastic Gradient Optimization Techniques · Markov Chains and Monte Carlo Methods
