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Yang P. Liu

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29 papers
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29

FOCS Conference 2025 Conference Paper

On Inverse Theorems and Combinatorial Lines

  • Amey Bhangale
  • Subhash Khot
  • Yang P. Liu
  • Dor Minzer

The problem of studying k-wise correlations in product spaces, i. e. , correlations of the form ${\mathbb{E}_{\left( {{x_1}, \ldots, {x_k}} \right)\sim \mu \otimes n}}\left[ {{f_1}\left( {{x_1}} \right) \cdots f\left( {{x_k}} \right)} \right]$ where ${\text{ }}{f_i}: \sum\nolimits_i^n \to \mathbb{C}$ are all 1-bounded functions and µ is a distribution over Σ 1 × … × Σ k, appears in many different contexts throughout discrete mathematics. Examples include additive combinatorics, extremal combinatorics, hardness of approximation and probability. The goal in an inverse theorem is to characterize the type of functions f 1, …, f k that achieve non-trivial correlations, under minimal assumptions on the distribution µ. We give new inverse theorems for k-wise correlations for all k ⩾ 3. For k = 3, our inverse theorem works for any distribution µ which is pairwise-connected, which is essentially the minimal assumption required for a nontrivial inverse theorem to hold. For k > 3, our inverse theorem applies for distributions µ satisfying the stronger condition of not having any Abelian embeddings. This resolves a conjecture from [Bhangale-Khot-Minzer, STOC 2022]. We give applications of our inverse theorems to additive combinatorics, hardness of approximation, and property testing. First, we show that there exists c > 0 such that any set A ⊆ {0, 1, 2} n with density at least Ω((loglogloglogn) −c ) must contain a combinatorial line, i. e. , x, y, z ∈ {0, 1, 2} n, not all equal, such that x i = y i = z i or (x i, y i, z i ) = (0, 1, 2) for all i = 1, 2, …, n. In other words, we give "reasonable bounds" for the density Hales-Jewett theorem of length 3. This involves combining our inverse theorems with several additional insights, motivated by Shkredov’s proof of the corners theorem and Polymath’s combinatorial proof of the density Hales-Jewett theorem. Second, we show how to construct a dictatorship vs quasi-random test that has perfect completeness and soundness s + ε from integrality gap instances with similar parameters, provided that its local distributions have no Abelian embeddings. Third, we analyze the direct-sum tester of [Dinur-Golubev, RANDOM 2019] in the low-soundness regime.

FOCS Conference 2025 Conference Paper

Quasipolynomial Bounds for the Corners Theorem

  • Michael Jaber
  • Yang P. Liu
  • Shachar Lovett
  • Anthony Ostuni
  • Mehtaab Sawhney

Let G be a finite abelian group and A be a subset of $G \times G$ which is corner-free, meaning that there are no $x, y \in G$ and $d \in G \backslash\{0\}$ such that $(x, y), (x+d, y), (x, y+d) \in A$. We prove that \begin{equation*}|A| \leq|G|^{2} \cdot \exp \left(-(\log |G|)^{\Omega{1}}\right)\end{equation*}As a consequence, we obtain polynomial (in the input length) lower bounds on the non-deterministic communication complexity of Exactly-N in the 3-player Number-on-Forehead model. We also obtain the first “reasonable” lower bounds on the coloring version of the 3 -dimensional corners problem, as well as on the non-deterministic communication complexity of Exactly-N in the 4-player Number-on-Forehead model. This is an extended abstract. The full version of the paper can be found at https: //arxiv. org/abs/2504. 07006.

FOCS Conference 2024 Conference Paper

Almost-Linear Time Algorithms for Decremental Graphs: Min-Cost Flow and More via Duality

  • Jan van den Brand
  • Li Chen 0028
  • Rasmus Kyng
  • Yang P. Liu
  • Simon Meierhans
  • Maximilian Probst Gutenberg
  • Sushant Sachdeva

We give the first almost-linear total time algorithm for deciding if a flow of cost at most $F$ still exists in a directed graph, with edge costs and capacities, undergoing decremental updates, i. e. , edge deletions, capacity decreases, and cost increases. This implies almost-linear time algorithms for approximating the minimum-cost flow value and s-t distance on such decremental graphs. Our framework additionally allows us to maintain decremental strongly connected components in almost-linear time deterministically. These algorithms also improve over the current best known runtimes for statically computing minimum-cost flow, in both the randomized and deterministic settings. We obtain our algorithms by taking the dual perspective, which yields cut-based algorithms. More precisely, our algorithm computes the flow via a sequence of $m^{1+o(1)}$ -dynamic min-ratio cut problems, the dual analog of the dynamic min-ratio cycle problem that underlies recent fast algorithms for minimum-cost flow. Our main technical contribution is a new data structure that returns an approximately optimal min-ratio cut in amortized $m^{o(1)}$ time by maintaining a tree-cut sparsifier. This is achieved by devising a new algorithm to maintain the dynamic expander hierarchy of [ $\text{Goranci-Racke-}$ SaranurakTan, SODA 2021] that also works in capacitated graphs. All our algorithms are deterministc, though they can be sped up further using randomized techniques while still working against an adaptive adversary.

STOC Conference 2024 Conference Paper

Almost-Linear Time Algorithms for Incremental Graphs: Cycle Detection, SCCs, s-t Shortest Path, and Minimum-Cost Flow

  • Li Chen 0028
  • Rasmus Kyng
  • Yang P. Liu
  • Simon Meierhans
  • Maximilian Probst Gutenberg

We give the first almost-linear time algorithms for several problems in incremental graphs including cycle detection, strongly connected component maintenance, s - t shortest path, maximum flow, and minimum-cost flow. To solve these problems, we give a deterministic data structure that returns a m o (1) -approximate minimum-ratio cycle in fully dynamic graphs in amortized m o (1) time per update. Combining this with the interior point method framework of Brand-Liu-Sidford (STOC 2023) gives the first almost-linear time algorithm for deciding the first update in an incremental graph after which the cost of the minimum-cost flow attains value at most some given threshold F . By rather direct reductions to minimum-cost flow, we are then able to solve the problems in incremental graphs mentioned above. Our new data structure also leads to a modular and deterministic almost-linear time algorithm for minimum-cost flow by removing the need for complicated modeling of a restricted adversary, in contrast to the recent randomized and deterministic algorithms for minimum-cost flow in Chen-Kyng-Liu-Peng-Probst Gutenberg-Sachdeva (FOCS 2022)Brand-Chen-Kyng-Liu-Peng-Probst Gutenberg-Sachdeva-Sidford (FOCS 2023). At a high level, our algorithm dynamizes the ℓ 1 oblivious routing of Rozhoň-Grunau-Haeupler-Zuzic-Li (STOC 2022), and develops a method to extract an approximate minimum ratio cycle from the structure of the oblivious routing. To maintain the oblivious routing, we use tools from concurrent work of Kyng-Meierhans-Probst Gutenberg (STOC 2024) which designed vertex sparsifiers for shortest paths, in order to maintain a sparse neighborhood cover in fully dynamic graphs. To find a cycle, we first show that an approximate minimum ratio cycle can be represented as a fundamental cycle on a small set of trees resulting from the oblivious routing. Then, we find a cycle whose quality is comparable to the best tree cycle. This final cycle query step involves vertex and edge sparsification procedures reminiscent of the techniques introduced in Chen-Kyng-Liu-Peng-Probst Gutenberg-Sachdeva (FOCS 2022), but crucially requires a more powerful dynamic spanner, which can handle far more edge insertions than prior work. We build such a spanner via a construction that hearkens back to the classic greedy spanner algorithm of Althöfer-Das-Dobkin-Joseph-Soares (DiscreteComputational Geometry 1993).

FOCS Conference 2024 Conference Paper

On Approximate Fully-Dynamic Matching and Online Matrix-Vector Multiplication

  • Yang P. Liu

We study connections between the problem of fully dynamic $(1-\epsilon)$ -approximate maximum bipartite matching, and the dual $(1+\epsilon)$ -approximate vertex cover problem, with the online matrix-vector (OMv) conjecture which has recently been used in several fine-grained hardness reductions. We prove that there is an online algorithm that maintains a $(1+\epsilon)$ -approximate vertex cover in amortized $n^{1-c}\epsilon^{-C}$ time for constants $c, C > 0$ for fully dynamic updates if and only if the OMv conjecture is false. Similarly, we prove that there is an online algorithm that maintains a $(1-\epsilon)$ -approximate maximum matching in amortized $n^{1-c}\epsilon^{-C}$ time if and only if there is a nontrivial algorithm for another dynamic problem, which we call dynamic approximate OMv, that has seemingly no matching structure. This provides some evidence against achieving amortized sublinear update times for approximate fully dynamic matching and vertex cover. Leveraging these connections, we obtain faster algorithms for approximate fully dynamic matching in both the online and offline settings. We give a randomized algorithm that with high probability maintains a $(1-\epsilon)$ -approximate bipartite matching and $(1+\epsilon)$ -approximate vertex cover in fully dynamic graphs, in amortized $O(\epsilon^{-O(1)}\frac{n}{2^{\Omega}(\sqrt{\log n})})$ up-date time. This improves over the previous fastest runtimes of $O(n/(\log^{*}n)^{\Omega(1)})$ due to Assadi-Behnezhad-Khanna-Li [STOC 2023], and $O_{\epsilon}(n^{1-\Omega_{\epsilon}(1)})$ due to Bhattacharya-Kiss-Saranurak [FOCS 2023] for small $\epsilon$. Our algorithm leverages fast algorithms for OMv due to Larsen and Williams [SODA 2017]. We give a randomized offline algorithm for (1 - $\epsilon)$ -approximate maximum matching with amortized runtime $O(n^{. 58}\epsilon^{-O(1)})$ by using fast matrix multi-plication, significantly improving over the runtimes achieved via online algorithms mentioned above. This mirrors the situation with OMv, where an offline algorithm exactly corresponds to fast matrix mul-tiplication. We also give an offline algorithm that maintains a $(1+\epsilon)$ -approximate vertex cover in amortized $O(n^{. 723}\epsilon^{-O(1)})$ time.

STOC Conference 2024 Conference Paper

Sparsifying Generalized Linear Models

  • Arun Jambulapati
  • James R. Lee
  • Yang P. Liu
  • Aaron Sidford

We consider the sparsification of sums F : ℝ n → ℝ + where F ( x ) = f 1 (⟨ a 1 , x ⟩) + ⋯ + f m (⟨ a m , x ⟩) for vectors a 1 ,…, a m ∈ ℝ n and functions f 1 ,…, f m : ℝ → ℝ + . We show that (1+ε)-approximate sparsifiers of F with support size n /ε 2 (log n /ε) O (1) exist whenever the functions f 1 ,…, f m are symmetric, monotone, and satisfy natural growth bounds. Additionally, we give efficient algorithms to compute such a sparsifier assuming each f i can be evaluated efficiently. Our results generalize the classical case of ℓ p sparsification, where f i ( z ) = | z | p , for p ∈ (0, 2], and give the first near-linear size sparsifiers in the well-studied setting of the Huber loss function and its generalizations, e.g., f i ( z ) = min{| z | p , | z | 2 } for 0 < p ≤ 2. Our sparsification algorithm can be applied to give near-optimal reductions for optimizing a variety of generalized linear models including ℓ p regression for p ∈ (1, 2] to high accuracy, via solving (log n ) O (1) sparse regression instances with m ≤ n (log n ) O (1) , plus runtime proportional to the number of nonzero entries in the vectors a 1 , …, a m .

FOCS Conference 2023 Conference Paper

A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow

  • Jan van den Brand
  • Li Chen 0028
  • Richard Peng
  • Rasmus Kyng
  • Yang P. Liu
  • Maximilian Probst Gutenberg
  • Sushant Sachdeva
  • Aaron Sidford

We give a deterministic $m^{1+o(1)}$ time algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially bounded integral demands, costs, and capacities. As a consequence, we obtain the first running time improvement for deterministic algorithms that compute maximum-flow in graphs with polynomial bounded capacities since the work of Goldberg-Rao [J. ACM ’98]. Our algorithm builds on the framework of Chen-Kyng-Liu-Peng-Gutenberg-Sachdeva [FOCS ’22] that computes an optimal flow by computing a sequence of $m^{1+o(1)}$-approximate undirected minimum-ratio cycles. We develop a deterministic dynamic graph data-structure to compute such a sequence of minimum-ratio cycles in an amortized $m^{o(1)}$ time per edge update. Our key technical contributions are deterministic analogues of the vertex sparsification and edge sparsification components of the data-structure from Chen et al. For the vertex sparsification component, we give a method to avoid the randomness in Chen et al. which involved sampling random trees to recurse on. For the edge sparsification component, we design a deterministic algorithm that maintains an embedding of a dynamic graph into a sparse spanner. We also show how our dynamic spanner can be applied to give a deterministic data structure that maintains a fully dynamic low-stretch spanning tree on graphs with polynomially bounded edge lengths, with subpolynomial average stretch and subpolynomial amortized time per edge update.

STOC Conference 2023 Conference Paper

Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification

  • Arun Jambulapati
  • Yang P. Liu
  • Aaron Sidford

We present an algorithm that given any n -vertex, m -edge, rank r hypergraph constructs a spectral sparsifier with O ( n ε −2 log n log r ) hyperedges in nearly-linear O ( mr ) time. This improves in both size and efficiency over a line of work [Bansal-Svensson-Trevisan 2019, Kapralov-Krauthgamer-Tardos-Yoshida 2021] for which the previous best size was O (min{ n ε −4 log 3 n , nr 3 ε −2 log n }) and runtime was O ( mr + n O (1) ).

FOCS Conference 2023 Conference Paper

Sparsifying Sums of Norms

  • Arun Jambulapati
  • James R. Lee
  • Yang P. Liu
  • Aaron Sidford

Abstract-For any norms $N_{1}, \ldots, N_{m}$ on $\mathbb{R}^{n}$ and $N(x): = N_{1}(x)+\cdots+N_{m}(x)$, we show there is a sparsified norm $\tilde{N}(x)= w_{1} N_{1}(x)+\cdots+w_{m} N_{m}(x)$ such that $|N(x)-\tilde{N}(x)| \leqslant \varepsilon N(x)$ for all $x \in \mathbb{R}^{n}$, where $w_{1}, \ldots, w_{m}$ are non-negative weights, of which only $O\left(\varepsilon^{-2} n \log (n / \varepsilon)(\log n)^{2. 5}\right)$ are non-zero. Additionally, we show that such weights can be found with high probability in time $O\left(m(\log n)^{O(1)}+\right. $ poly $\left. (n)\right) T$, where T is the time required to evaluate a norm $N_{i}(x)$, assuming that $N(x)$ is poly $(n)$ equivalent to the Euclidean norm. This immediately yields analogous statements for sparsifying sums of symmetric submodular functions. More generally, we show how to sparsify sums of p th powers of norms when the sum is p-uniformly smooth. 1

FOCS Conference 2022 Conference Paper

Maximum Flow and Minimum-Cost Flow in Almost-Linear Time

  • Li Chen 0028
  • Rasmus Kyng
  • Yang P. Liu
  • Richard Peng
  • Maximilian Probst Gutenberg
  • Sushant Sachdeva

We give an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially bounded integral demands, costs, and capacities in $m^{1+o(1)}$ time. Our algorithm builds the flow through a sequence of $m^{1+o(1)}$ approximate undirected minimum-ratio cycles, each of which is computed and processed in amortized $m^{o(1)}$ time using a new dynamic graph data structure. Our framework extends to algorithms running in $m^{1+o(1)}$ time for computing flows that minimize general edge-separable convex functions to high accuracy. This gives almost-linear time algorithms for several problems including entropy-regularized optimal transport, matrix scaling, p-norm flows, and p-norm isotonic regression on arbitrary directed acyclic graphs.

FOCS Conference 2022 Conference Paper

Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence

  • Nima Anari
  • Yang P. Liu
  • Thuy-Duong Vuong

We design fast algorithms for repeatedly sampling from strongly Rayleigh distributions, which include as special cases random spanning tree distributions and determinantal point processes. For a graph $G=(V, \ E)$, we show how to approximately sample uniformly random spanning trees from G in $O(|V|)$ 1 time per sample after an initial $O(|E|)$ time preprocessing. This is the first nearly-linear runtime in the output size, which is clearly optimal. For a determinantal point process on k-sized subsets of a ground set of n elements, defined via an $n\times n$ kernel matrix, we show how to approximately sample in ${\widetilde{O}}(k^{\omega})$ time after an initial ${\widetilde{O}}(nk^{\omega-1})$ time preprocessing, where $\omega\lt 2. 372864$ is the matrix multiplication exponent. The time to compute just the weight of the output set is simply $\simeq k^{\omega}$, a natural barrier that suggests our runtime might be optimal for determinantal point processes as well. As a corollary, we even improve the state of the art for obtaining a single sample from a determinantal point process, from the prior runtime of ${\widetilde{O}}(\min\{nk^{2}, \ n^{\omega}\})$ to ${\widetilde{O}}(nk^{\omega-1})$. In our main technical result, we achieve the optimal limit on domain sparsification for strongly Rayleigh distributions. In domain sparsification, sampling from a distribution $\mu$ on $\binom{[n]}{k}$ is reduced to sampling from related distributions on $\binom{[t]}{k}$ for $t\ll n$. We show that for strongly Rayleigh distributions, the domain size can be reduced to nearly linear in the output size $t={\widetilde{O}}(k)$, improving the state of the art from $t={\widetilde{O}}(k^{2})$ for general strongly Rayleigh distributions and the more specialized $t={\widetilde{O}}(k^{15})$ for sBanning tree distributions. Our reduction involves sampling from ${\widetilde{O}}(1)$ domain-sparsified distributions, all of which can be produced efficiently assuming approximate overestimates for marginals of $\mu$ are known and stored in a convenient data structure. Having access to marginals is the discrete analog of having access to the mean and covariance of a continuous distribution, or equivalently knowing “isotropy” for the distribution, the key behind optimal samplers in the continuous setting based on the famous Kannan-Lovász-Simonovits (KLS) conjecture. We view our result as analogous in spirit to the KLS conjecture and its consequences for sampling, but rather for discrete strongly Rayleigh measures. 1 Throughout, ${\widetilde{O}}(\cdot)$ hides polylogarithmic factors in n.

FOCS Conference 2021 Conference Paper

Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao

  • Yu Gao 0001
  • Yang P. Liu
  • Richard Peng

We give an algorithm for computing exact maximum flows on graphs with $m$ edges and integer capacities in the range [ $1, U$ ] in $\tilde{O}(m^{\frac{3}{2}-\frac{1}{328}}\log U)$ time. 1 1 We use $\tilde{O}(\cdot)$ to suppress logarithmic factors in $m$. For sparse graphs with polynomially bounded integer capacities, this is the first improvement over the $\tilde{O}(m^{1. 5}\log U)$ time bound from [Goldberg-Rao JACM '98]. Our algorithm revolves around dynamically maintaining the augmenting electrical flows at the core of the interior point method based algorithm from [Mądry JACM '16]. This entails designing data structures that, in limited settings, return edges with large electric energy in a graph undergoing resistance updates.

FOCS Conference 2021 Conference Paper

Minor Sparsifiers and the Distributed Laplacian Paradigm

  • Sebastian Forster
  • Gramoz Goranci
  • Yang P. Liu
  • Richard Peng
  • Xiaorui Sun
  • Mingquan Ye

We study distributed algorithms built around minor-based vertex sparsifiers, and give the first algorithm in the CONGEST model for solving linear systems in graph Laplacian matrices to high accuracy. Our Laplacian solver has a round complexity of $O(n^{o(1)}(\sqrt{n}+D))$, and thus almost matches the lower bound of $\widetilde{\Omega}(\sqrt{n}+D)$, where $n$ is the number of nodes in the network and $D$ is its diameter. We show that our distributed solver yields new sublinear round algorithms for several cornerstone problems in combinatorial optimization. This is achieved by leveraging the powerful algorithmic framework of Interior Point Methods (IPMs) and the Laplacian paradigm in the context of distributed graph algorithms, which entails numerically solving optimization problems on graphs via a series of Laplacian systems. Problems that benefit from our distributed algorithmic paradigm include exact mincost flow, negative weight shortest paths, maxflow, and bipartite matching on sparse directed graphs. For the maxflow problem, this is the first exact distributed algorithm that applies to directed graphs, while the previous work by [Ghaffari et al. SICOMP'18] considered the approximate setting and works only for undirected graphs. For the mincost flow and the negative weight shortest path problems, our results constitute the first exact distributed algorithms running in a sublinear number of rounds. Given that the hybrid between IPMs and the Laplacian paradigm has proven useful for tackling numerous optimization problems in the centralized setting, we believe that our distributed solver will find future applications. At the heart of our distributed Laplacian solver is the notion of spectral subspace sparsifiers of [Li, Schild FOCS'18]. We present a nontrivial distributed implementation of their construction by (i) giving a parallel variant of their algorithm that avoids the sampling of random spanning trees and uses approximate leverage scores instead, and (ii) showing that the algorithm still produces a high-quality subspace spectral sparsifier by carefully setting up and analyzing matrix martingales. Combining this vertex reduction recursively with both tree and elimination-based preconditioners leads to our algorithm for solving Laplacian systems. The construction of the elimination-based preconditioners is based on computing short random walks, and we introduce a new technique for reducing the congestion incurred by the simulation of these walks on weighted graphs.

SODA Conference 2021 Conference Paper

Vertex Sparsification for Edge Connectivity

  • Parinya Chalermsook
  • Syamantak Das
  • Yunbum Kook
  • Bundit Laekhanukit
  • Yang P. Liu
  • Richard Peng
  • Mark Sellke
  • Daniel Vaz 0001

Graph compression or sparsification is a basic information-theoretic and computational question. A major open problem in this research area is whether (1 + ∊ )-approximate cut-preserving vertex sparsifiers with size close to the number of terminals exist. As a step towards this goal, we study a thresholded version of the problem: for a given parameter c, find a smaller graph, which we call connectivity-c mimicking network, which preserves connectivity among k terminals exactly up to the value of c. We show that connectivity- c mimicking networks with O ( kc 4 ) edges exist and can be found in time m ( c log n ) O ( c ). We also give a separate algorithm that constructs such graphs with k · O ( c ) 2 c edges in time mc O ( c ) log O (1) n. These results lead to the first data structures for answering fully dynamic offline c -edge-connectivity queries for c ≥ 4 in polylogarithmic time per query, as well as more efficient algorithms for survivable network design on bounded treewidth graphs.

STOC Conference 2020 Conference Paper

Faster energy maximization for faster maximum flow

  • Yang P. Liu
  • Aaron Sidford

In this paper we provide an algorithm which given any m -edge n -vertex directed graph with integer capacities at most U computes a maximum s - t flow for any vertices s and t in m 11/8+ o (1) U 1/4 time with high probability. This running time improves upon the previous best of Õ( m 10/7 U 1/7 ) (Mądry 2016), Õ( m √ n log U ) (Lee Sidford 2014), and O ( mn ) (Orlin 2013) when the graph is not too dense or has large capacities. We achieve this result by leveraging recent advances in solving undirected flow problems on graphs. We show that in the maximum flow framework of (Mądry 2016) the problem of optimizing the amount of perturbation of the central path needed to maximize energy and thereby reduce congestion can be efficiently reduced to a smoothed ℓ 2 -ℓ p flow optimization problem, which can be solved approximately via recent work (Kyng, Peng, Sachdeva, Wang 2019). Leveraging this new primitive, we provide a new interior point method for maximum flow with faster convergence and simpler analysis that no longer needs global potential functions involving energy as in previous methods (Mądry 2013, Mądry 2016).

FOCS Conference 2020 Conference Paper

Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time

  • Tarun Kathuria
  • Yang P. Liu
  • Aaron Sidford

We present an algorithm, which given any m-edge n-vertex directed graph with positive integer capacities at most U computes a maximum s-t flow for any vertices s and t in O(m 4/3+o(1) U 1/3 ) time. This improves upon the previous best running times of O(m 11/8+o(1) U 1/4 ) [1], Õ(m√nlogU) [2] and O(mn) [3] when the graph is not too dense and doesn't have large capacities. We build upon advances for sparse maxflow based on interior point methods [1], [4], [5]. Whereas these methods increase the energy of local ℓ 2 -norm minimizing electrical flows, we instead increase the Bregman divergence value of flows which minimize the Bregman divergence with respect to a weighted log barrier. This allows us to trace the central path with progress depending only on ℓ ∞ norm bounds on the congestion vector as opposed to the ℓ 4 norm, which arises in these prior works. Further, we show that smoothed ℓ 2 -ℓ p flows [6], [7] which were used to maximize energy [1] can also be used to efficiently maximize divergence, thereby yielding our desired runtimes. We believe our approach towards Bregman divergences of barriers may be of further interest.

FOCS Conference 2019 Conference Paper

Parallel Reachability in Almost Linear Work and Square Root Depth

  • Yang P. Liu
  • Arun Jambulapati
  • Aaron Sidford

In this paper we provide a parallel algorithm that given any n-node m-edge directed graph and source vertex s computes all vertices reachable from s with Õ(m) work and n {1/2 + o(1) } depth with high probability in n. This algorithm also computes a set of Õ(n) edges which when added to the graph preserves reachability and ensures that the diameter of the resulting graph is at most n {1/2 + o(1) }. Our result improves upon the previous best known almost linear work reachability algorithm due to Fineman [1] which had depth Õ(n 2/3 ). Further, we show how to leverage this algorithm to achieve improved distributed algorithms for single source reachability in the CONGEST model. In particular, we provide a distributed algorithm that given a n-node digraph of undirected hop-diameter D solves the single source reachability problem with Õ(n 1/2 + n 1/3+o(1) D 2/3 ) rounds of the communication in the CONGEST model with high probability in n. Our algorithm is nearly optimal whenever D = O(n 1/4-ε ) for any constant ε > 0 and is the first nearly optimal algorithm for general graphs whose diameter is Ω(n δ ) for any constant δ.

SODA Conference 2019 Conference Paper

Reproducibility and Pseudo-Determinism in Log-Space

  • Ofer Grossman
  • Yang P. Liu

A curious property of randomized log-space search algorithms is that their outputs are often longer than their workspace. This leads to the question: how can we reproduce the results of a randomized log space computation without storing the output or randomness verbatim? Running the algorithm again with new random bits may result in a new (and potentially different) output. We show that every problem in search-RL has a randomized log-space algorithm where the output can be reproduced. Specifically, we show that for every problem in search-RL, there are a pair of log-space randomized algorithms A and B where for every input x, A will output some string t x of size O (log n ), such that B when running on ( x, t x ) will be pseudo-deterministic: that is, running B multiple times on the same input ( x, t x ) will result in the same output on all executions with high probability. Thus, by storing only O (log n ) bits in memory, it is possible to reproduce the output of a randomized log-space algorithm. An algorithm is reproducible without storing any bits in memory (i. e. , | t x | = 0) if and only if it is pseudo-deterministic. We show pseudo-deterministic algorithms for finding paths in undirected graphs and Eulerian graphs using logarithmic space. Our algorithms are substantially faster than the best known deterministic algorithms for finding paths in such graphs in log-space. The algorithm for search-RL has the additional property that its output, when viewed as a random variable depending on the randomness used by the algorithm, has entropy O (log n ).

SODA Conference 2019 Conference Paper

Short Cycles via Low-Diameter Decompositions

  • Yang P. Liu
  • Sushant Sachdeva
  • Zejun Yu

We present improved algorithms for short cycle decomposition of a graph – a decomposition of an undirected, unweighted graph into edge-disjoint cycles, plus a small number of additional edges. Short cycle decompositions were introduced in the recent work of Chu et al. (FOCS 2018), and were used to make progress on several questions in graph sparsification. For all constants δ ∊ (0, 1], we give an O ( mn δ ) time algorithm that, given a graph G, partitions its edges into cycles of length, with O ( n ) extra edges not in any cycle. This gives the first subquadratic, in fact almost linear time, algorithm achieving polylogarithmic cycle lengths. We also give an m · time algorithm that partitions the edges of a graph into cycles of length, with O ( n ) extra edges not in any cycle. This improves on the short cycle decomposition algorithms given by Chu et al. in terms of all parameters, and is significantly simpler. As a result, we obtain faster algorithms and improved guarantees for several problems in graph sparsification – construction of resistance sparsifiers, graphical spectral sketches, degree preserving sparsifiers, and approximating the effective resistances of all edges.