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1. Computation of Cycle Bases in Surface Embedded Graphs

   Fox, Kyle ; Stanley, Thomas ( December 2022 , 33rd International Symposium on Algorithms and Computation)

   Bae, Sang Won ; Park, Heejin (Ed.)

   We present an O(n³ g² log g + m) + Õ(n^{ω + 1}) time deterministic algorithm to find the minimum cycle basis of a directed graph embedded on an orientable surface of genus g. This result improves upon the previous fastest known running time of O(m³ n + m² n² log n) applicable to general directed graphs. While an O(n^ω + 2^{2g} n² + m) time deterministic algorithm was known for undirected graphs, the use of the underlying field ℚ in the directed case (as opposed to ℤ₂ for the undirected case) presents extra challenges. It turns out that some of our new observations are useful for both variants of the problem, so we present an O(n^ω + n² g² log g + m) time deterministic algorithm for undirected graphs as well. 

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2. A Deterministic Algorithm for Balanced Cut with Applications to Dynamic Connectivity, Flows, and Beyond

   https://doi.org/10.1109/FOCS46700.2020.00111  

   Chuzhoy, Julia ; Gao, Yu ; Li, Jason ; Nanongkai, Danupon ; Peng, Richard ; Saranurak, Thatchaphol ( November 2020 , 61st IEEE Annual Symposium on Foundations of Computer Science, FOCS 2020, Durham, NC, USA, November 16-19, 2020)

   null (Ed.)

   We consider the classical Minimum Balanced Cut problem: given a graph $G$, compute a partition of its vertices into two subsets of roughly equal volume, while minimizing the number of edges connecting the subsets. We present the first {\em deterministic, almost-linear time} approximation algorithm for this problem. Specifically, our algorithm, given an $n$-vertex $m$-edge graph $G$ and any parameter $1\leq r\leq O(\log n)$, computes a $(\log m)^{r^2}$-approximation for Minimum Balanced Cut on $G$, in time $O\left ( m^{1+O(1/r)+o(1)}\cdot (\log m)^{O(r^2)}\right )$. In particular, we obtain a $(\log m)^{1/\epsilon}$-approximation in time $m^{1+O(1/\sqrt{\epsilon})}$ for any constant $\epsilon$, and a $(\log m)^{f(m)}$-approximation in time $m^{1+o(1)}$, for any slowly growing function $m$. We obtain deterministic algorithms with similar guarantees for the Sparsest Cut and the Lowest-Conductance Cut problems. Our algorithm for the Minimum Balanced Cut problem in fact provides a stronger guarantee: it either returns a balanced cut whose value is close to a given target value, or it certifies that such a cut does not exist by exhibiting a large subgraph of $G$ that has high conductance. We use this algorithm to obtain deterministic algorithms for dynamic connectivity and minimum spanning forest, whose worst-case update time on an $n$-vertex graph is $n^{o(1)}$, thus resolving a major open problem in the area of dynamic graph algorithms. Our work also implies deterministic algorithms for a host of additional problems, whose time complexities match, up to subpolynomial in $n$ factors, those of known randomized algorithms. The implications include almost-linear time deterministic algorithms for solving Laplacian systems and for approximating maximum flows in undirected graphs. 

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3. Constructing many faces in arrangements of lines and segments

   https://doi.org/10.20382/jocg.v14i1a11  

   Wang, Haitao ( December 2023 , Journal of computational geometry)

   We present new algorithms for computing many faces in arrangements of lines and segments. Given a set $S$ of $n$ lines (resp., segments) and a set $P$ of $m$ points in the plane, the problem is to compute the faces of the arrangements of $S$ that contain at least one point of $P$. For the line case, we give a deterministic algorithm of $O(m^{2/3}n^{2/3}\log^{2/3} (n/\sqrt{m})+(m+n)\log n)$ time. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\log^{2.22}n$ and improves the previously best randomized algorithm [Agarwal, Matoušek, and Schwarzkopf, 1998] by a factor of $\log^{1/3}n$ in certain cases (e.g., when $m=\Theta(n)$). For the segment case, we present a deterministic algorithm of $O(n^{2/3}m^{2/3}\log n+\tau(n\alpha^2(n)+n\log m+m)\log n)$ time, where $\tau=\min\{\log m,\log (n/\sqrt{m})\}$ and $\alpha(n)$ is the inverse Ackermann function. This improves the previously best deterministic algorithm [Agarwal, 1990] by a factor of $\log^{2.11}n$ and improves the previously best randomized algorithm [Agarwal, Matoušek, and Schwarzkopf, 1998] by a factor of $\log n$ in certain cases (e.g., when $m=\Theta(n)$). We also give a randomized algorithm of $O(m^{2/3}K^{1/3}\log n+\tau(n\alpha(n)+n\log m+m)\log n\log K)$ expected time, where $K$ is the number of intersections of all segments of $S$. In addition, we consider the query version of the problem, that is, preprocess $S$ to compute the face of the arrangement of $S$ that contains any given query point. We present new results that improve the previous work for both the line and the segment cases. In particulary, for the line case, we build a data structure of $O(n\log n)$ space in $O(n\log n)$ randomized time, so that the face containing the query point can be obtained in $O(\sqrt{n\log n})$ time with high probability (more specifically, the query returns a binary search tree representing the face so that standard binary-search-based queries on the face can be handled in $O(\log n)$ time each and the face itself can be output explicitly in time linear in its size). 

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4. A deterministic near-linear time approximation scheme for geometric transportation

   https://doi.org/10.1109/FOCS57990.2023.00078  

   Fox, Emily ; Lu, Jiashuai ( November 2023 , IEEE)

   Given a set of points $P = (P^+ \sqcup P^-) \subset \mathbb{R}^d$ for some constant $d$ and a supply function $\mu:P\to \mathbb{R}$ such that $\mu(p) > 0~\forall p \in P^+$, $\mu(p) < 0~\forall p \in P^-$, and $\sum_{p\in P}{\mu(p)} = 0$, the geometric transportation problem asks one to find a transportation map $\tau: P^+\times P^-\to \mathbb{R}_{\ge 0}$ such that $\sum_{q\in P^-}{\tau(p, q)} = \mu(p)~\forall p \in P^+$, $\sum_{p\in P^+}{\tau(p, q)} = -\mu(q) \forall q \in P^-$, and the weighted sum of Euclidean distances for the pairs $\sum_{(p,q)\in P^+\times P^-}\tau(p, q)\cdot ||q-p||_2$ is minimized. We present the first deterministic algorithm that computes, in near-linear time, a transportation map whose cost is within a $(1 + \varepsilon)$ factor of optimal. More precisely, our algorithm runs in $O(n\varepsilon^{-(d+2)}\log^5{n}\log{\log{n}})$ time for any constant $\varepsilon > 0$. While a randomized $n\varepsilon^{-O(d)}\log^{O(d)}{n}$ time algorithm for this problem was discovered in the last few years, all previously known deterministic $(1 + \varepsilon)$-approximation algorithms run in~$\Omega(n^{3/2})$ time. A similar situation existed for geometric bipartite matching, the special case of geometric transportation where all supplies are unit, until a deterministic $n\varepsilon^{-O(d)}\log^{O(d)}{n}$ time $(1 + \varepsilon)$-approximation algorithm was presented at STOC 2022. Surprisingly, our result is not only a generalization of the bipartite matching one to arbitrary instances of geometric transportation, but it also reduces the running time for all previously known $(1 + \varepsilon)$-approximation algorithms, randomized or deterministic, even for geometric bipartite matching. In particular, we give the first $(1 + \varepsilon)$-approximate deterministic algorithm for geometric bipartite matching and the first $(1 + \varepsilon)$-approximate deterministic or randomized algorithm for geometric transportation with no dependence on $d$ in the exponent of the running time's polylog. As an additional application of our main ideas, we also give the first randomized near-linear $O(\varepsilon^{-2} m \log^{O(1)} n)$ time $(1 + \varepsilon)$-approximation algorithm for the uncapacitated minimum cost flow (transshipment) problem in undirected graphs with arbitrary \emph{real} edge costs. 

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5. A deterministic algorithm for finding r-power divisors

   https://doi.org/10.1007/s40993-022-00387-w  

   Harvey, David ; Hittmeir, Markus ( December 2022 , Research in Number Theory)

   Abstract Building on work of Boneh, Durfee and Howgrave-Graham, we present a deterministic algorithm that provably finds all integers p such that $$p^r \mathrel {|}N$$ p r | N in time $$O(N^{1/4r+\epsilon })$$ O ( N 1 / 4 r + ϵ ) for any $$\epsilon > 0$$ ϵ > 0 . For example, the algorithm can be used to test squarefreeness of N in time $$O(N^{1/8+\epsilon })$$ O ( N 1 / 8 + ϵ ) ; previously, the best rigorous bound for this problem was $$O(N^{1/6+\epsilon })$$ O ( N 1 / 6 + ϵ ) , achieved via the Pollard–Strassen method. 

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