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Drucker (2012) proved the following result: Unless the unlikely complexity-theoretic collapse coNP is in NP/poly occurs, there is no AND-compression for SAT. The result has implications for the compressibility and kernelizability of a whole…

Computational Complexity · Computer Science 2018-04-24 Holger Dell

We study the problem of predicting as well as the best linear predictor in a bounded Euclidean ball with respect to the squared loss. When only boundedness of the data generating distribution is assumed, we establish that the least squares…

Statistics Theory · Mathematics 2021-03-09 Tomas Vaškevičius , Nikita Zhivotovskiy

We introduce a smooth variant of the SCAD thresholding rule for wavelet denoising by replacing its piecewise linear transition with a raised cosine. The resulting shrinkage function is odd, continuous on R, and continuously differentiable…

Computation · Statistics 2026-01-19 Radhika Kulkarni , Aluisio Pinheiro , Brani Vidakovic , Abdourrahmane M. Atto

Let $(M,g)$ be an $n$-dimensional compact boudaryless Riemannian manifold with nonpositive sectional curvature, then our conclusion is that we can give improved estimates for the $L^p$ norms of the restrictions of eigenfunctions to smooth…

Analysis of PDEs · Mathematics 2012-10-31 Xuehua Chen

A line of work initiated by Fortnow in 1997 has proven model-independent time-space lower bounds for the $\mathsf{SAT}$ problem and related problems within the polynomial-time hierarchy. For example, for the $\mathsf{SAT}$ problem, the…

Computational Complexity · Computer Science 2021-02-01 Abhijit S. Mudigonda , R. Ryan Williams

We prove a quantum ergodicity theorem in position space for the eigenfunctions of a Schr\"odinger operator $-\Delta+V$ on a rectangular torus $\mathbb{T}^2$ for $V\in L^2(\mathbb{T}^2)$ with an algebraic rate of convergence in terms of the…

Mathematical Physics · Physics 2023-09-18 Henrik Ueberschaer

The problem of minimizing the maximum of $N$ convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring $O(N\epsilon^{-2/3} + \epsilon^{-8/3})$…

Quantum Physics · Physics 2024-02-21 Hao Wang , Chenyi Zhang , Tongyang Li

We show that whenever $s>k(k+1)$, then for any complex sequence $(\mathfrak a_n)_{n\in \mathbb Z}$, one has $$\int_{[0,1)^k}\left| \sum_{|n|\le N}\mathfrak a_ne(\alpha_1n+\ldots +\alpha_kn^k) \right|^{2s}\,{\rm d}{\mathbf \alpha}\ll…

Classical Analysis and ODEs · Mathematics 2024-07-01 Trevor D. Wooley

The nonnegative rank of a matrix A is the smallest integer r such that A can be written as the sum of r rank-one nonnegative matrices. The nonnegative rank has received a lot of attention recently due to its application in optimization,…

Optimization and Control · Mathematics 2016-08-10 Hamza Fawzi , Pablo A. Parrilo

Let $O$ be chosen uniformly at random from the group of $(N+L) \times (N+L)$ orthogonal matrices. Denote by $\tilde{O}$ the upper-left $N \times N$ corner of $O$, which we refer to as a truncation of $O$. In this paper we prove two…

Probability · Mathematics 2025-04-18 Alex Little , Francesco Mezzadri , Nick Simm

The Chernoff bound is one of the most widely used tools in theoretical computer science. It's rare to find a randomized algorithm that doesn't employ a Chernoff bound in its analysis. The standard proofs of Chernoff bounds are beautiful but…

Data Structures and Algorithms · Computer Science 2026-02-10 William Kuszmaul

A proof for the lower bound is provided for the smallest eigenvalue of finite element equations with arbitrary conforming simplicial meshes. The bound has a similar form as the one by Graham and McLean [SIAM J. Numer. Anal., 44 (2006), pp.…

Numerical Analysis · Mathematics 2021-06-24 Lennard Kamenski

We show that any quantum circuit of treewidth $t$, built from $r$-qubit gates, requires at least $\Omega(\frac{n^{2}}{2^{O(r\cdot t)}\cdot \log^4 n})$ gates to compute the element distinctness function. Our result generalizes a…

Computational Complexity · Computer Science 2016-10-03 Mateus de Oliveira Oliveira

We present two randomised approximate counting algorithms with $\widetilde{O}(n^{2-c}/\varepsilon^2)$ running time for some constant $c>0$ and accuracy $\varepsilon$: (1) for the hard-core model with fugacity $\lambda$ on graphs with…

Data Structures and Algorithms · Computer Science 2025-01-15 Konrad Anand , Weiming Feng , Graham Freifeld , Heng Guo , Jiaheng Wang

In order to formally understand the power of neural computing, we first need to crack the frontier of threshold circuits with two and three layers, a regime that has been surprisingly intractable to analyze. We prove the first super-linear…

Computational Complexity · Computer Science 2018-02-01 Daniel M. Kane , Ryan Williams

Let $(M,g)$ be a two-dimensional compact boundaryless Riemannian manifold with nonpostive curvature, then we shall give improved estimates for the $L^2$-norms of the restrictions of eigenfunctions to unit-length geodesics, compared to the…

Analysis of PDEs · Mathematics 2011-09-12 Christopher D. Sogge , Steve Zelditch

We consider the bound-constrained global optimization of functions with low effective dimensionality, that are constant along an (unknown) linear subspace and only vary over the effective (complement) subspace. We aim to implicitly explore…

Optimization and Control · Mathematics 2020-09-23 Coralia Cartis , Estelle Massart , Adilet Otemissov

Cheeger's fundamental inequality states that any edge-weighted graph has a vertex subset $S$ such that its expansion (a.k.a. conductance) is bounded as follows: \[ \phi(S) \defeq \frac{w(S,\bar{S})}{\min \set{w(S), w(\bar{S})}} \leq…

Data Structures and Algorithms · Computer Science 2015-03-19 Anand Louis , Prasad Raghavendra , Prasad Tetali , Santosh Vempala

We consider random Schr\"odinger operators with Dirichlet boundary conditions outside lattice approximations of a smooth Euclidean domain and study the behavior of its lowest-lying eigenvalues in the limit when the lattice spacing tends to…

Probability · Mathematics 2018-07-04 Marek Biskup , Ryoki Fukushima , Wolfgang Koenig

Grothendieck constants $K_G(d)$ bound the advantage of $d$-dimensional strategies over $1$-dimensional ones in a specific optimisation task. They have applications ranging from approximation algorithms to quantum nonlocality. However, apart…

Optimization and Control · Mathematics 2026-02-03 Sébastien Designolle , Tamás Vértesi , Sebastian Pokutta