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1. Tight Bounds for ℓ ₁ Oblivious Subspace Embeddings

   https://doi.org/10.1145/3477537  

   Wang, Ruosong; Woodruff, David P. (January 2022, ACM Transactions on Algorithms)

   An \ell _p oblivious subspace embedding is a distribution over r \times n matrices \Pi such that for any fixed n \times d matrix A , \[ \Pr _{\Pi }[\textrm {for all }x, \ \Vert Ax\Vert _p \le \Vert \Pi Ax\Vert _p \le \kappa \Vert Ax\Vert _p] \ge 9/10,\] where r is the dimension of the embedding, \kappa is the distortion of the embedding, and for an n -dimensional vector y , \Vert y\Vert _p = (\sum _{i=1}^n |y_i|^p)^{1/p} is the \ell _p -norm. Another important property is the sparsity of \Pi , that is, the maximum number of non-zero entries per column, as this determines the running time of computing \Pi A . While for p = 2 there are nearly optimal tradeoffs in terms of the dimension, distortion, and sparsity, for the important case of 1 \le p \lt 2 , much less was known. In this article, we obtain nearly optimal tradeoffs for \ell _1 oblivious subspace embeddings, as well as new tradeoffs for 1 \lt p \lt 2 . Our main results are as follows: (1) We show for every 1 \le p \lt 2 , any oblivious subspace embedding with dimension r has distortion \[ \kappa = \Omega \left(\frac{1}{\left(\frac{1}{d}\right)^{1 / p} \log ^{2 / p}r + \left(\frac{r}{n}\right)^{1 / p - 1 / 2}}\right).\] When r = {\operatorname{poly}}(d) \ll n in applications, this gives a \kappa = \Omega (d^{1/p}\log ^{-2/p} d) lower bound, and shows the oblivious subspace embedding of Sohler and Woodruff (STOC, 2011) for p = 1 is optimal up to {\operatorname{poly}}(\log (d)) factors. (2) We give sparse oblivious subspace embeddings for every 1 \le p \lt 2 . Importantly, for p = 1 , we achieve r = O(d \log d) , \kappa = O(d \log d) and s = O(\log d) non-zero entries per column. The best previous construction with s \le {\operatorname{poly}}(\log d) is due to Woodruff and Zhang (COLT, 2013), giving \kappa = \Omega (d^2 {\operatorname{poly}}(\log d)) or \kappa = \Omega (d^{3/2} \sqrt {\log n} \cdot {\operatorname{poly}}(\log d)) and r \ge d \cdot {\operatorname{poly}}(\log d) ; in contrast our r = O(d \log d) and \kappa = O(d \log d) are optimal up to {\operatorname{poly}}(\log (d)) factors even for dense matrices. We also give (1) \ell _p oblivious subspace embeddings with an expected 1+\varepsilon number of non-zero entries per column for arbitrarily small \varepsilon \gt 0 , and (2) the first oblivious subspace embeddings for 1 \le p \lt 2 with O(1) -distortion and dimension independent of n . Oblivious subspace embeddings are crucial for distributed and streaming environments, as well as entrywise \ell _p low-rank approximation. Our results give improved algorithms for these applications. 

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2. A completion of the proof of the Edge-statistics Conjecture

   https://doi.org/10.19086/aic.12047  

   Fox, Jacob; Sauermann, Lisa (February 2020, Advances in Combinatorics)

   Extremal combinatorics often deals with problems of maximizing a specific quantity related to substructures in large discrete structures. The first question of this kind that comes to one's mind is perhaps determining the maximum possible number of induced subgraphs isomorphic to a fixed graph $$H$$ in an $$n$$-vertex graph. The asymptotic behavior of this number is captured by the limit of the ratio of the maximum number of induced subgraphs isomorphic to $$H$$ and the number of all subgraphs with the same number vertices as $$H$$; this quantity is known as the _inducibility_ of $$H$$. More generally, one can define the inducibility of a family of graphs in the analogous way.Among all graphs with $$k$$ vertices, the only two graphs with inducibility equal to one are the empty graph and the complete graph. However, how large can the inducibility of other graphs with $$k$$ vertices be? Fix $$k$$, consider a graph with $$n$$ vertices join each pair of vertices independently by an edge with probability $$\binom{k}{2}^{-1}$$. The expected number of $$k$$-vertex induced subgraphs with exactly one edge is $$e^{-1}+o(1)$$. So, the inducibility of large graphs with a single edge is at least $$e^{-1}+o(1)$$. This article establishes that this bound is the best possible in the following stronger form, which proves a conjecture of Alon, Hefetz, Krivelevich and Tyomkyn: the inducibility of the family of $$k$$-vertex graphs with exactly $$l$$ edges where $$0<\binom{k}{2}$$ is at most $$e^{-1}+o(1)$$. The example above shows that this is tight for $l=1$ and it can be also shown to be tight for $l=k-1$. The conjecture was known to be true in the regime where $$l$$ is superlinearly bounded away from $$0$$ and $$\binom{k}{2}$$, for which the sum of the inducibilities goes to zero, and also in the regime where $$l$$ is bounded away from $$0$$ and $$\binom{k}{2}$$ by a sufficiently large linear function. The article resolves the hardest cases where $$l$$ is linearly close to $$0$$ or close to $$\binom{k}{2}$$, and provides generalizations to hypergraphs. 

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3. Near-Optimal Hypergraph Sparsification in Insertion-Only and Bounded-Deletion Streams

   https://doi.org/10.4230/lipics.icalp.2025.108  

   Khanna, Sanjeev; Putterman, Aaron; Sudan, Madhu (June 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Censor-Hillel, Keren; Grandoni, Fabrizio; Ouaknine, Joel; Puppis, Gabriele (Ed.)

   We study the problem of constructing hypergraph cut sparsifiers in the streaming model where a hypergraph on n vertices is revealed either via an arbitrary sequence of hyperedge insertions alone (insertion-only streaming model) or via an arbitrary sequence of hyperedge insertions and deletions (dynamic streaming model). For any ε ∈ (0,1), a (1 ± ε) hypergraph cut-sparsifier of a hypergraph H is a reweighted subgraph H' whose cut values approximate those of H to within a (1 ± ε) factor. Prior work shows that in the static setting, one can construct a (1 ± ε) hypergraph cut-sparsifier using Õ(nr/ε²) bits of space [Chen-Khanna-Nagda FOCS 2020], and in the setting of dynamic streams using Õ(nrlog m/ε²) bits of space [Khanna-Putterman-Sudan FOCS 2024]; here the Õ notation hides terms that are polylogarithmic in n, and we use m to denote the total number of hyperedges in the hypergraph. Up until now, the best known space complexity for insertion-only streams has been the same as that for the dynamic streams. This naturally poses the question of understanding the complexity of hypergraph sparsification in insertion-only streams. Perhaps surprisingly, in this work we show that in insertion-only streams, a (1 ± ε) cut-sparsifier can be computed in Õ(nr/ε²) bits of space, matching the complexity of the static setting. As a consequence, this also establishes an Ω(log m) factor separation between the space complexity of hypergraph cut sparsification in insertion-only streams and dynamic streams, as the latter is provably known to require Ω(nr log m) bits of space. To better explain this gap, we then show a more general result: namely, if the stream has at most k hyperedge deletions then Õ(n r log k/ε²) bits of space suffice for hypergraph cut sparsification. Thus the space complexity smoothly interpolates between the insertion-only regime (k = 0) and the fully dynamic regime (k = m). Our algorithmic results are driven by a key technical insight: once sufficiently many hyperedges have been inserted into the stream (relative to the number of allowed deletions), we can significantly reduce the underlying hypergraph by size by irrevocably contracting large subsets of vertices. Finally, we complement this result with an essentially matching lower bound of Ω(n r log(k/n)) bits, thus providing essentially a tight characterization of the space complexity for hypergraph cut-sparsification across a spectrum of streaming models. 

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4. Improved Bounds on a Generalization of Tuza's Conjecture

   https://doi.org/10.37236/10829  

   Basit, Abdul; McGinnis, Daniel; Simmons, Henry; Sinnwell, Matt; Zerbib, Shira (October 2022, The Electronic Journal of Combinatorics)

   For an $$r$$-uniform hypergraph $$H$$, let $$\nu^{(m)}(H)$$ denote the maximum size of a set $$M$$ of edges in $$H$$ such that every two edges in $$M$$ intersect in less than $$m$$ vertices, and let $$\tau^{(m)}(H)$$ denote the minimum size of a collection $$C$$ of $$m$$-sets of vertices such that every edge in $$H$$ contains an element of $$C$$. The fractional analogues of these parameters are denoted by $$\nu^{*(m)}(H)$$ and $$\tau^{*(m)}(H)$$, respectively. Generalizing a famous conjecture of Tuza on covering triangles in a graph, Aharoni and Zerbib conjectured that for every $$r$$-uniform hypergraph $$H$$, $$\tau^{(r-1)}(H)/\nu^{(r-1)}(H) \leq \lceil{\frac{r+1}{2}}\rceil$$. In this paper we prove bounds on the ratio between the parameters $$\tau^{(m)}$$ and $$\nu^{(m)}$$, and their fractional analogues. Our main result is that, for every $$r$$-uniform hypergraph~$$H$$,\[ \tau^{*(r-1)}(H)/\nu^{(r-1)}(H) \le \begin{cases} \frac{3}{4}r - \frac{r}{4(r+1)} &\text{for }r\text{ even,}\\\frac{3}{4}r - \frac{r}{4(r+2)} &\text{for }r\text{ odd.} \\\end{cases} \]This improves the known bound of $$r-1$.We also prove that, for every $$r$$-uniform hypergraph $$H$$, $$\tau^{(m)}(H)/\nu^{*(m)}(H) \le \operatorname{ex}_m(r, m+1)$$, where the Turán number $$\operatorname{ex}_r(n, k)$$ is the maximum number of edges in an $$r$$-uniform hypergraph on $$n$$ vertices that does not contain a copy of the complete $$r$$-uniform hypergraph on $$k$$ vertices. Finally, we prove further bounds in the special cases $(r,m)=(4,2)$ and $(r,m)=(4,3)$. 

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5. A Spanner for the Day After

   https://doi.org/10.1007/s00454-020-00228-6  

   Buchin, Kevin; Har-Peled, Sariel; Oláh, Dániel (December 2020, Discrete & Computational Geometry)

   null (Ed.)

   Abstract We show how to construct a $$(1+\varepsilon )$$ ( 1 + ε ) -spanner over a set $${P}$$ P of n points in $${\mathbb {R}}^d$$ R d that is resilient to a catastrophic failure of nodes. Specifically, for prescribed parameters $${\vartheta },\varepsilon \in (0,1)$$ ϑ , ε ∈ ( 0 , 1 ) , the computed spanner $${G}$$ G has $$\begin{aligned} {{\mathcal {O}}}\bigl (\varepsilon ^{-O(d)} {\vartheta }^{-6} n(\log \log n)^6 \log n \bigr ) \end{aligned}$$ O ( ε - O ( d ) ϑ - 6 n ( log log n ) 6 log n ) edges. Furthermore, for any k , and any deleted set $${{B}}\subseteq {P}$$ B ⊆ P of k points, the residual graph $${G}\setminus {{B}}$$ G \ B is a $$(1+\varepsilon )$$ ( 1 + ε ) -spanner for all the points of $${P}$$ P except for $$(1+{\vartheta })k$$ ( 1 + ϑ ) k of them. No previous constructions, beyond the trivial clique with $${{\mathcal {O}}}(n^2)$$ O ( n 2 ) edges, were known with this resilience property (i.e., only a tiny additional fraction of vertices, $$\vartheta |B|$$ ϑ | B | , lose their distance preserving connectivity). Our construction works by first solving the exact problem in one dimension, and then showing a surprisingly simple and elegant construction in higher dimensions, that uses the one-dimensional construction in a black-box fashion. 

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