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1. Almost All Alternating Groups are Invariably Generated by Two Elements of Prime Order

   https://doi.org/10.1093/imrn/rnac354  

   Teräväinen, Joni (January 2023, International Mathematics Research Notices)

   Abstract We show that for all $$n\leq X$$ apart from $$O(X\exp (-c(\log X)^{1/2}(\log \log X)^{1/2}))$$ exceptions, the alternating group $$A_{n}$$ is invariably generated by two elements of prime order. This answers (in a quantitative form) a question of Guralnick, Shareshian, and Woodroofe. 

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2. Sometimes Reliable Spanners of Almost Linear Size

   Buchin, Kevin; Har-Peled, Sariel; Olah, Daniel (January 2020, 28th Annual European Symposium on Algorithms (ESA))

   Grandoni, Fabrizio; Herman, Grzegorz; Sanders, Peter (Ed.)

   Reliable spanners can withstand huge failures, even when a linear number of vertices are deleted from the network. In case of failures, some of the remaining vertices of a reliable spanner may no longer admit the spanner property, but this collateral damage is bounded by a fraction of the size of the attack. It is known that Ω(nlog n) edges are needed to achieve this strong property, where n is the number of vertices in the network, even in one dimension. Constructions of reliable geometric (1+ε)-spanners, for n points in ℝ^d, are known, where the resulting graph has 𝒪(n log n log log⁶n) edges. Here, we show randomized constructions of smaller size spanners that have the desired reliability property in expectation or with good probability. The new construction is simple, and potentially practical - replacing a hierarchical usage of expanders (which renders the previous constructions impractical) by a simple skip list like construction. This results in a 1-spanner, on the line, that has linear number of edges. Using this, we present a construction of a reliable spanner in ℝ^d with 𝒪(n log log²n log log log n) edges. 

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3. Properly Learning Decision Trees in almost Polynomial Time

   Blanc, Guy; Lange, Jane; Qiao, Mingda; Tan, Li-Yang (January 2022, Journal of the Association for Computing Machinery)

   We give an nO(log log n)-time membership query algorithm for properly and agnostically learning decision trees under the uniform distribution over { ± 1}n. Even in the realizable setting, the previous fastest runtime was nO(log n), a consequence of a classic algorithm of Ehrenfeucht and Haussler. Our algorithm shares similarities with practical heuristics for learning decision trees, which we augment with additional ideas to circumvent known lower bounds against these heuristics. To analyze our algorithm, we prove a new structural result for decision trees that strengthens a theorem of O’Donnell, Saks, Schramm, and Servedio. While the OSSS theorem says that every decision tree has an influential variable, we show how every decision tree can be “pruned” so that every variable in the resulting tree is influential. 

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4. Almost Optimal Exact Distance Oracles for Planar Graphs

   https://doi.org/10.1145/3580474  

   Charalampopoulos, Panagiotis; Gawrychowski, Paweł; Long, Yaowei; Mozes, Shay; Pettie, Seth; Weimann, Oren; Wulff-Nilsen, Christian (April 2023, Journal of the ACM)

   We consider the problem of preprocessing a weighted directed planar graph in order to quickly answer exact distance queries. The main tension in this problem is between space S and query time Q , and since the mid-1990s all results had polynomial time-space tradeoffs, e.g., Q = ~ Θ( n/√ S ) or Q = ~Θ( n 5/2 /S 3/2 ). In this article we show that there is no polynomial tradeoff between time and space and that it is possible to simultaneously achieve almost optimal space n 1+ o (1) and almost optimal query time n o (1) . More precisely, we achieve the following space-time tradeoffs: n 1+ o (1) space and log 2+ o (1) n query time, n log 2+ o (1) n space and n o (1) query time, n 4/3+ o (1) space and log 1+ o (1) n query time. We reduce a distance query to a variety of point location problems in additively weighted Voronoi diagrams and develop new algorithms for the point location problem itself using several partially persistent dynamic tree data structures. 

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5. Summing $$\mu (n)$$: a faster elementary algorithm

   https://doi.org/10.1007/s40993-022-00408-8  

   Helfgott, Harald Andrés; Thompson, Lola (March 2023, Research in Number Theory)

   Abstract We present a new elementary algorithm that takes $$ \textrm{time} \ \ O_\epsilon \left( x^{\frac{3}{5}} (\log x)^{\frac{8}{5}+\epsilon } \right) \ \ \textrm{and} \ \textrm{space} \ \ O\left( x^{\frac{3}{10}} (\log x)^{\frac{13}{10}} \right) $$ time O ϵ x 3 5 ( log x ) 8 5 + ϵ and space O x 3 10 ( log x ) 13 10 (measured bitwise) for computing $$M(x) = \sum _{n \le x} \mu (n),$$ M ( x ) = ∑ n ≤ x μ ( n ) , where $$\mu (n)$$ μ ( n ) is the Möbius function. This is the first improvement in the exponent of x for an elementary algorithm since 1985. We also show that it is possible to reduce space consumption to $$O(x^{1/5} (\log x)^{5/3})$$ O ( x 1 / 5 ( log x ) 5 / 3 ) by the use of (Helfgott in: Math Comput 89:333–350, 2020), at the cost of letting time rise to the order of $$x^{3/5} (\log x)^2 \log \log x$$ x 3 / 5 ( log x ) 2 log log x . 

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