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1. Efficient Convex Optimization Requires Superlinear Memory

   https://doi.org/10.1145/3689208  

   Marsden, Annie; Sharan, Vatsal; Sidford, Aaron; Valiant, Gregory (December 2024, Journal of the ACM)

   We show that any memory-constrained, first-order algorithm which minimizes d-dimensional, 1-Lipschitz convex functions over the unit ball to 1/poly(d) accuracy using at most $$d^{1.25-\delta}$$ bits of memory must make at least $$\tilde{Omega}(d^{1+(4/3)\delta})$$ first-order queries (for any constant $$\delta in [0,1/4]$$). Consequently, the performance of such memory-constrained algorithms are a polynomial factor worse than the optimal $$\tilde{O}(d)$$ query bound for this problem obtained by cutting plane methods that use $$\tilde{O}(d^2)$$ memory. This resolves a COLT 2019 open problem of Woodworth and Srebro. 

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2. Memory-Sample Tradeoffs for Linear Regression with Small Error

   Sharan, Vatsal; Sidford, Aaron; Valiant, Gregory (January 2019, Symposium on Theory of Computing (STOC))

   We consider the problem of performing linear regression over a stream of d-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples (a_1,b_1), (a_2,b_2)..., with a_i drawn independently from a d-dimensional isotropic Gaussian, and where b_i = + \eta_i, for a fixed x in R^d with ||x||= 1 and with independent noise \eta_i drawn uniformly from the interval [-2^{-d/5},2^{-d/5}]. We show that any algorithm with at most d^2/4 bits of memory requires at least \Omega(d \log \log \frac{1}{\epsilon}) samples to approximate x to \ell_2 error \epsilon with probability of success at least 2/3, for \epsilon sufficiently small as a function of d. In contrast, for such \epsilon, x can be recovered to error \epsilon with probability 1-o(1) with memory O\left(d^2 \log(1/\epsilon)\right) using d examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization. 

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3. Irregular subgraphs

   https://doi.org/10.1017/S0963548322000220  

   Alon, Noga; Wei, Fan (March 2023, Combinatorics, Probability and Computing)

   Abstract We suggest two related conjectures dealing with the existence of spanning irregular subgraphs of graphs. The first asserts that any $$d$$ -regular graph on $$n$$ vertices contains a spanning subgraph in which the number of vertices of each degree between $$0$$ and $$d$$ deviates from $$\frac{n}{d+1}$$ by at most $$2$$ . The second is that every graph on $$n$$ vertices with minimum degree $$\delta$$ contains a spanning subgraph in which the number of vertices of each degree does not exceed $$\frac{n}{\delta +1}+2$$ . Both conjectures remain open, but we prove several asymptotic relaxations for graphs with a large number of vertices $$n$$ . In particular we show that if $$d^3 \log n \leq o(n)$$ then every $$d$$ -regular graph with $$n$$ vertices contains a spanning subgraph in which the number of vertices of each degree between $$0$$ and $$d$$ is $$(1+o(1))\frac{n}{d+1}$$ . We also prove that any graph with $$n$$ vertices and minimum degree $$\delta$$ contains a spanning subgraph in which no degree is repeated more than $$(1+o(1))\frac{n}{\delta +1}+2$$ times. 

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4. Affine Stresses, Inverse Systems, and Reconstruction Problems

   https://doi.org/10.1093/imrn/rnad294  

   Murai, Satoshi; Novik, Isabella; Zheng, Hailun (December 2023, International Mathematics Research Notices)

   Abstract A conjecture of Kalai asserts that for $$d\geq 4$$, the affine type of a prime simplicial $$d$$-polytope $$P$$ can be reconstructed from the space of affine $$2$$-stresses of $$P$$. We prove this conjecture for all $$d\geq 5$$. We also prove the following generalization: for all pairs $(i,d)$ with $$2\leq i\leq \lceil \frac d 2\rceil -1$$, the affine type of a simplicial $$d$$-polytope $$P$$ that has no missing faces of dimension $$\geq d-i+1$$ can be reconstructed from the space of affine $$i$$-stresses of $$P$$. A consequence of our proofs is a strengthening of the Generalized Lower Bound Theorem: it was proved by Nagel that for any simplicial $(d-1)$-sphere $$\Delta $$ and $$1\leq k\leq \lceil \frac {d}{2}\rceil -1$$, $$g_{k}(\Delta )$$ is at least as large as the number of missing $(d-k)$-faces of $$\Delta $$; here we show that, for $$1\leq k\leq \lfloor \frac {d}{2}\rfloor -1$$, equality holds if and only if $$\Delta $$ is $$k$$-stacked. Finally, we show that for $$d\geq 4$$, any simplicial $$d$$-polytope $$P$$ that has no missing faces of dimension $$\geq d-1$$ is redundantly rigid, that is, for each edge $$e$$ of $$P$$, there exists an affine $$2$$-stress on $$P$$ with a non-zero value on $$e$$. 

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5. A trade-off between classical and quantum circuit size for an attack against CSIDH

   https://doi.org/10.1515/jmc-2020-0070  

   Biasse, Jean-François; Bonnetain, Xavier; Pring, Benjamin; Schrottenloher, André; Youmans, William (November 2020, Journal of Mathematical Cryptology)

   null (Ed.)

   Abstract We propose a heuristic algorithm to solve the underlying hard problem of the CSIDH cryptosystem (and other isogeny-based cryptosystems using elliptic curves with endomorphism ring isomorphic to an imaginary quadratic order 𝒪). Let Δ = Disc(𝒪) (in CSIDH, Δ = −4 p for p the security parameter). Let 0 < α < 1/2, our algorithm requires: A classical circuit of size 2 O ˜ log ( | Δ | ) 1 − α . $$2^{\tilde{O}\left(\log(|\Delta|)^{1-\alpha}\right)}.$$ A quantum circuit of size 2 O ˜ log ( | Δ | ) α . $$2^{\tilde{O}\left(\log(|\Delta|)^{\alpha}\right)}.$$ Polynomial classical and quantum memory. Essentially, we propose to reduce the size of the quantum circuit below the state-of-the-art complexity 2 O ˜ log ( | Δ | ) 1 / 2 $$2^{\tilde{O}\left(\log(|\Delta|)^{1/2}\right)}$$ at the cost of increasing the classical circuit-size required. The required classical circuit remains subexponential, which is a superpolynomial improvement over the classical state-of-the-art exponential solutions to these problems. Our method requires polynomial memory, both classical and quantum. 

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