In this paper we consider generalized flow problems where there is an m-edge n-node directed graph $G=(V, E)$ and each edge $e \in E$ has a loss factor $\gamma_{e}\gt 0$ governing whether the flow is increased or decreased as it crosses edge e. We provide a randomized $\widetilde{O}\left(\left(m+n^{1.5}\right) \cdot \operatorname{polylog}\left(\frac{W}{\delta}\right)\right)$ time algorithm for solving the generalized maximum flow and generalized minimum cost flow problems in this setting where $\delta$ is the target accuracy and W is the maximum of all costs, capacities, and loss factors and their inverses. This improves upon the previous state-of-the-art $\widetilde{O}\left(m \sqrt{n} \cdot \log ^{2}\left(\frac{W}{\delta}\right)\right)$ time algorithm, obtained by combining the algorithm of [17] with techniques from [29]. To obtain this result we provide new dynamic data structures and spectral results regarding the matrices associated to generalized flows and apply them through the interior point method framework of [39].1.1The full version of this paper is available at https://arxiv.org/abs/2510.17740.

In numerical linear algebra, considerable effort has been devoted to obtaining faster algorithms for linear systems whose underlying matrices exhibit structural properties. A prominent success story is the method of generalized nested dissection [Lipton-Rose-Tarjan’79] for separable matrices. On the other hand, the majority of recent developments in the design of efficient linear program (LP) solvers have not leveraged the ideas underlying these faster linear system solvers nor exploited the separable structure of the constraint matrix.

We demonstrate that for expander graphs, for all ε > 0, there exists a data structure of size Õ(nε-1) which can be used to return (1 + ε)-approximations to effective resistances in Õ(1) time per query. Short of storing all effective resistances, previous best approaches could achieve Õ(nε-2) size and Õ (ε-2) time per query by storing Johnson-Lindenstrauss vectors for each vertex, or Õ (nε-1) size and Õ (nε-1) time per query by storing a spectral sketch.

Imagine a large graph that is being processed by a cluster of computers, e.g., described by the k-machine model or the Massively Parallel Computation Model. The graph, however, is not static; instead it is receiving a constant stream of updates. How fast can the cluster process the stream of updates? The fundamental question we want to ask in this paper is whether we can update the graph fast enough to keep up with the stream.