Equiangular lines via matrix projection
- URL: http://arxiv.org/abs/2110.15842v4
- Date: Mon, 5 Feb 2024 21:53:56 GMT
- Title: Equiangular lines via matrix projection
- Authors: Igor Balla
- Abstract summary: In 1973, Lemmens and Seidel posed the problem of determining the maximum number of equiangular lines in $mathbbRr$ with angle $arccos(alpha)$.
Recent breakthroughs have led to an almost complete resolution of this problem.
We introduce a new method for obtaining upper bounds which unifies and improves upon previous approaches.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In 1973, Lemmens and Seidel posed the problem of determining the maximum
number of equiangular lines in $\mathbb{R}^r$ with angle $\arccos(\alpha)$ and
gave a partial answer in the regime $r \leq 1/\alpha^2 - 2$. At the other
extreme where $r$ is at least exponential in $1/\alpha$, recent breakthroughs
have led to an almost complete resolution of this problem. In this paper, we
introduce a new method for obtaining upper bounds which unifies and improves
upon previous approaches, thereby yielding bounds which bridge the gap between
the aforementioned regimes and are best possible either exactly or up to a
small multiplicative constant. Our approach relies on orthogonal projection of
matrices with respect to the Frobenius inner product and as a byproduct, it
yields the first extension of the Alon-Boppana theorem to dense graphs, with
equality for strongly regular graphs corresponding to $\binom{r+1}{2}$
equiangular lines in $\mathbb{R}^r$. Applications of our method in the complex
setting will be discussed as well.
Related papers
- Guessing Efficiently for Constrained Subspace Approximation [49.83981776254246]
We introduce a general framework for constrained subspace approximation.
We show it provides new algorithms for partition-constrained subspace approximation with applications to $k$-means clustering, and projected non-negative matrix factorization.
arXiv Detail & Related papers (2025-04-29T15:56:48Z) - When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time? [22.047262762274414]
The goal is to find two low-rank matrices $U, V in mathbbRn times k$ such that the cost of $| W circ (U Vtop - A) |_F2$ is minimized.
In this work, we show that there is a certain regime, even if $A$ and $W$ are dense, we can still hope to solve the weighted low-rank approximation problem in almost linear $n1+o(1)$ time.
arXiv Detail & Related papers (2025-02-24T07:18:24Z) - The Communication Complexity of Approximating Matrix Rank [50.6867896228563]
We show that this problem has randomized communication complexity $Omega(frac1kcdot n2log|mathbbF|)$.
As an application, we obtain an $Omega(frac1kcdot n2log|mathbbF|)$ space lower bound for any streaming algorithm with $k$ passes.
arXiv Detail & Related papers (2024-10-26T06:21:42Z) - Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks [46.04785603483612]
We prove that Nesterov's accelerated gradient attains an complexity $O(kappalogfrac1epsilon)$.
In particular, we prove that NAG can also attain an accelerated linear convergence rate.
arXiv Detail & Related papers (2024-10-12T20:33:37Z) - Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms [50.15964512954274]
We study the problem of residual error estimation for matrix and vector norms using a linear sketch.
We demonstrate that this gives a substantial advantage empirically, for roughly the same sketch size and accuracy as in previous work.
We also show an $Omega(k2/pn1-2/p)$ lower bound for the sparse recovery problem, which is tight up to a $mathrmpoly(log n)$ factor.
arXiv Detail & Related papers (2024-08-16T02:33:07Z) - Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning [10.690769339903941]
We present a new class of preconditioned iterative methods for solving linear systems of the form $Ax = b$.
Our methods are based on constructing a low-rank Nystr"om approximation to $A$ using sparse random sketching.
We prove that the convergence of our methods depends on a natural average condition number of $A$, which improves as the rank of the Nystr"om approximation.
arXiv Detail & Related papers (2024-05-09T15:53:43Z) - Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems [9.30306458153248]
We consider the spectral tail condition number, $kappa_ell$, defined as the ratio between the $ell$th largest and the smallest singular value of the matrix representing the system.
Some of the implications of our result, and of the use of $kappa_ell$, include direct improvement over a fine-grained analysis of the Conjugate method.
arXiv Detail & Related papers (2024-05-09T14:56:49Z) - Provably learning a multi-head attention layer [55.2904547651831]
Multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models.
In this work, we initiate the study of provably learning a multi-head attention layer from random examples.
We prove computational lower bounds showing that in the worst case, exponential dependence on $m$ is unavoidable.
arXiv Detail & Related papers (2024-02-06T15:39:09Z) - Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems [61.85150061213987]
We study the generalized low-rank matrix bandit problem, proposed in citelu2021low under the Generalized Linear Model (GLM) framework.
To overcome the computational infeasibility and theoretical restrain of existing algorithms, we first propose the G-ESTT framework.
We show that G-ESTT can achieve the $tildeO(sqrt(d_1+d_2)3/2Mr3/2T)$ bound of regret while G-ESTS can achineve the $tildeO
arXiv Detail & Related papers (2024-01-14T14:14:19Z) - Hardness of Low Rank Approximation of Entrywise Transformed Matrix
Products [9.661328973620531]
Inspired by fast algorithms in natural language processing, we study low rank approximation in the entrywise transformed setting.
We give novel reductions from the Strong Exponential Time Hypothesis (SETH) that rely on lower bounding the leverage scores of flat sparse vectors.
Since our low rank algorithms rely on matrix-vectors, our lower bounds extend to show that computing $f(UV)W$, for even a small matrix $W$, requires $Omega(n2-o(1))$ time.
arXiv Detail & Related papers (2023-11-03T14:56:24Z) - Spectrum of inner-product kernel matrices in the polynomial regime and
multiple descent phenomenon in kernel ridge regression [3.997680012976965]
kernel matrix is well approximated by its degree-$ell$ approximation.
We show that the spectrum of the matrix converges in distribution to a Marchenko-Pastur law.
arXiv Detail & Related papers (2022-04-21T22:20:52Z) - Sketching Algorithms and Lower Bounds for Ridge Regression [65.0720777731368]
We give a sketching-based iterative algorithm that computes $1+varepsilon$ approximate solutions for the ridge regression problem.
We also show that this algorithm can be used to give faster algorithms for kernel ridge regression.
arXiv Detail & Related papers (2022-04-13T22:18:47Z) - Variance-Aware Confidence Set: Variance-Dependent Bound for Linear
Bandits and Horizon-Free Bound for Linear Mixture MDP [76.94328400919836]
We show how to construct variance-aware confidence sets for linear bandits and linear mixture Decision Process (MDP)
For linear bandits, we obtain an $widetildeO(mathrmpoly(d)sqrt1 + sum_i=1Ksigma_i2) regret bound, where $d is the feature dimension.
For linear mixture MDP, we obtain an $widetildeO(mathrmpoly(d)sqrtK)$ regret bound, where
arXiv Detail & Related papers (2021-01-29T18:57:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.