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1. On p-Group Isomorphism: Search-To-Decision, Counting-To-Decision, and Nilpotency Class Reductions via Tensors

   https://doi.org/10.4230/LIPIcs.CCC.2021.16  

   Grochow, Joshua A. ; Qiao, Youming ( August 2021 , 36th Computational Complexity Conference (CCC 2021))

   Kabanets, Valentine (Ed.)

   In this paper we study some classical complexity-theoretic questions regarding Group Isomorphism (GpI). We focus on p-groups (groups of prime power order) with odd p, which are believed to be a bottleneck case for GpI, and work in the model of matrix groups over finite fields. Our main results are as follows. - Although search-to-decision and counting-to-decision reductions have been known for over four decades for Graph Isomorphism (GI), they had remained open for GpI, explicitly asked by Arvind & Torán (Bull. EATCS, 2005). Extending methods from Tensor Isomorphism (Grochow & Qiao, ITCS 2021), we show moderately exponential-time such reductions within p-groups of class 2 and exponent p. - Despite the widely held belief that p-groups of class 2 and exponent p are the hardest cases of GpI, there was no reduction to these groups from any larger class of groups. Again using methods from Tensor Isomorphism (ibid.), we show the first such reduction, namely from isomorphism testing of p-groups of "small" class and exponent p to those of class two and exponent p. For the first results, our main innovation is to develop linear-algebraic analogues of classical graph coloring gadgets, a key technique in studying the structural complexity of GI. Unlike the graph coloring gadgets, which support restricting to various subgroups of the symmetric group, the problems we study require restricting to various subgroups of the general linear group, which entails significantly different and more complicated gadgets. The analysis of one of our gadgets relies on a classical result from group theory regarding random generation of classical groups (Kantor & Lubotzky, Geom. Dedicata, 1990). For the nilpotency class reduction, we combine a runtime analysis of the Lazard Correspondence with Tensor Isomorphism-completeness results (Grochow & Qiao, ibid.). 

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2. Unit-disk range searching and applications

   https://doi.org/10.20382/jocg.v14i1a13  

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

   Given a set $P$ of $n$ points in the plane, we consider the problem of computing the number of points of $P$ in a query unit disk (i.e., all query disks have the same radius). We show that the main techniques for simplex range searching in the plane can be adapted to this problem. For example, by adapting Matoušek's results, we can build a data structure of $O(n)$ space in $O(n^{1+\delta})$ time (for any $\delta>0$) so that each query can be answered in $O(\sqrt{n})$ time; alternatively, we can build a data structure of $O(n^2/\log^2 n)$ space with $O(n^{1+\delta})$ preprocessing time (for any $\delta>0$) and $O(\log n)$ query time. Our techniques lead to improvements for several other classical problems in computational geometry. 1. Given a set of $n$ unit disks and a set of $n$ points in the plane, the batched unit-disk range counting problem is to compute for each disk the number of points in it. Previous work [Katz and Sharir, 1997] solved the problem in $O(n^{4/3}\log n)$ time. We give a new algorithm of $O(n^{4/3})$ time, which is optimal as it matches an $\Omega(n^{4/3})$-time lower bound. For small $\chi$, where $\chi$ is the number of pairs of unit disks that intersect, we further improve the algorithm to $O(n^{2/3}\chi^{1/3}+n^{1+\delta})$ time, for any $\delta>0$. 2. The above result immediately leads to an $O(n^{4/3})$ time optimal algorithm for counting the intersecting pairs of circles for a set of $n$ unit circles in the plane. The previous best algorithms solve the problem in $O(n^{4/3}\log n)$ deterministic time [Katz and Sharir, 1997] or in $O(n^{4/3}\log^{2/3} n)$ expected time by a randomized algorithm [Agarwal, Pellegrini, and Sharir, 1993]. 3. Given a set $P$ of $n$ points in the plane and an integer $k$, the distance selection problem is to find the $k$-th smallest distance among all pairwise distances of $P$. The problem can be solved in $O(n^{4/3}\log^2 n)$ deterministic time [Katz and Sharir, 1997] or in $O(n\log n+n^{2/3}k^{1/3}\log^{5/3}n)$ expected time by a randomized algorithm [Chan, 2001]. Our new randomized algorithm runs in $O(n\log n +n^{2/3}k^{1/3}\log n)$ expected time. 4. Given a set $P$ of $n$ points in the plane, the discrete $2$-center problem is to compute two smallest congruent disks whose centers are in $P$ and whose union covers $P$. An $O(n^{4/3}\log^5 n)$-time algorithm was known [Agarwal, Sharir, and Welzl, 1998]. Our techniques yield a deterministic algorithm of $O(n^{4/3}\log^{10/3} n\cdot (\log\log n)^{O(1)})$ time and a randomized algorithm of $O(n^{4/3}\log^3 n\cdot (\log\log n)^{1/3})$ expected time. 

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3. 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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4. Bit Complexity of Jordan Normal Form and Polynomial Spectral Factorization

   Dey, Papri ; Kannan, Ravi ; Ryder, Nick ; Srivastava, Nikhil ( January 2023 , 14th Innovations in Theoretical Computer Science Conference (ITCS 2023))

   We study the bit complexity of two related fundamental computational problems in linear algebra and control theory. Our results are: (1) An Õ(n^{ω+3}a+n⁴a²+n^ωlog(1/ε)) time algorithm for finding an ε-approximation to the Jordan Normal form of an integer matrix with a-bit entries, where ω is the exponent of matrix multiplication. (2) An Õ(n⁶d⁶a+n⁴d⁴a²+n³d³log(1/ε)) time algorithm for ε-approximately computing the spectral factorization P(x) = Q^*(x)Q(x) of a given monic n× n rational matrix polynomial of degree 2d with rational a-bit coefficients having a-bit common denominators, which satisfies P(x)⪰0 for all real x. The first algorithm is used as a subroutine in the second one. Despite its being of central importance, polynomial complexity bounds were not previously known for spectral factorization, and for Jordan form the best previous best running time was an unspecified polynomial in n of degree at least twelve [Cai, 1994]. Our algorithms are simple and judiciously combine techniques from numerical and symbolic computation, yielding significant advantages over either approach by itself. 

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5. Matrix Multiplication Verification Using Coding Theory

   https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2024.42  

   Bennett, Huck ; Gajulapalli, Karthik ; Golovnev, Alexander ; Warton, Evelyn ( January 2024 , Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Kumar, Amit ; Ron-Zewi, Noga (Ed.)

   We study the Matrix Multiplication Verification Problem (MMV) where the goal is, given three n × n matrices A, B, and C as input, to decide whether AB = C. A classic randomized algorithm by Freivalds (MFCS, 1979) solves MMV in Õ(n²) time, and a longstanding challenge is to (partially) derandomize it while still running in faster than matrix multiplication time (i.e., in o(n^ω) time). To that end, we give two algorithms for MMV in the case where AB - C is sparse. Specifically, when AB - C has at most O(n^δ) non-zero entries for a constant 0 ≤ δ < 2, we give (1) a deterministic O(n^(ω-ε))-time algorithm for constant ε = ε(δ) > 0, and (2) a randomized Õ(n²)-time algorithm using δ/2 ⋅ log₂ n + O(1) random bits. The former algorithm is faster than the deterministic algorithm of Künnemann (ESA, 2018) when δ ≥ 1.056, and the latter algorithm uses fewer random bits than the algorithm of Kimbrel and Sinha (IPL, 1993), which runs in the same time and uses log₂ n + O(1) random bits (in turn fewer than Freivalds’s algorithm). Our algorithms are simple and use techniques from coding theory. Let H be a parity-check matrix of a Maximum Distance Separable (MDS) code, and let G = (I | G') be a generator matrix of a (possibly different) MDS code in systematic form. Our deterministic algorithm uses fast rectangular matrix multiplication to check whether HAB = HC and H(AB)^T = H(C^T), and our randomized algorithm samples a uniformly random row g' from G' and checks whether g'AB = g'C and g'(AB)^T = g'C^T. We additionally study the complexity of MMV. We first show that all algorithms in a natural class of deterministic linear algebraic algorithms for MMV (including ours) require Ω(n^ω) time. We also show a barrier to proving a super-quadratic running time lower bound for matrix multiplication (and hence MMV) under the Strong Exponential Time Hypothesis (SETH). Finally, we study relationships between natural variants and special cases of MMV (with respect to deterministic Õ(n²)-time reductions). 

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