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We provide new high-accuracy randomized algorithms for solving linear systems and regression problems that are well-conditioned except for $k$ large singular values. For solving such $d \times d$ positive definite system our algorithms…

Data Structures and Algorithms · Computer Science 2025-07-17 Michał Dereziński , Aaron Sidford

For each $n$, let $A_n=(\sigma_{ij})$ be an $n\times n$ deterministic matrix and let $X_n=(X_{ij})$ be an $n\times n$ random matrix with i.i.d. centered entries of unit variance. In the companion article Cook et al., we considered the…

Probability · Mathematics 2020-07-31 Nicholas A. Cook , Walid Hachem , Jamal Najim , David Renfrew

Semidefinite and sum-of-squares (SOS) optimization are fundamental computational tools in many areas, including linear and nonlinear systems theory. However, the scale of problems that can be addressed reliably and efficiently is still…

Optimization and Control · Mathematics 2022-02-17 Yang Zheng , Aivar Sootla , Antonis Papachristodoulou

Consider $N\times N$ symmetric one-dimensional random band matrices with general distribution of the entries and band width $W \geq N^{3/4+\varepsilon}$ for any $\varepsilon>0$. In the bulk of the spectrum and in the large $N$ limit, we…

Probability · Mathematics 2018-07-05 Paul Bourgade , Horng-Tzer Yau , Jun Yin

We introduce a notion of code sparsification that generalizes the notion of cut sparsification in graphs. For a (linear) code $\mathcal{C} \subseteq \mathbb{F}_q^n$ of dimension $k$ a $(1 \pm \epsilon)$-sparsification of size $s$ is given…

Data Structures and Algorithms · Computer Science 2023-11-03 Sanjeev Khanna , Aaron L Putterman , Madhu Sudan

We study the problem of solving semidefinite programs (SDP) in the streaming model. Specifically, $m$ constraint matrices and a target matrix $C$, all of size $n\times n$ together with a vector $b\in \mathbb{R}^m$ are streamed to us…

Data Structures and Algorithms · Computer Science 2023-09-12 Zhao Song , Mingquan Ye , Lichen Zhang

We define the notion of effective stiffness and show that it can used to build sparsifiers, algorithms that sparsify linear systems arising from finite-element discretizations of PDEs. In particular, we show that sampling $O(n\log n)$…

Numerical Analysis · Computer Science 2015-03-19 Haim Avron , Sivan Toledo

The classic graphical Cheeger inequalities state that if $M$ is an $n\times n$ symmetric doubly stochastic matrix, then \[ \frac{1-\lambda_{2}(M)}{2}\leq\phi(M)\leq\sqrt{2\cdot(1-\lambda_{2}(M))} \] where…

Combinatorics · Mathematics 2019-09-30 Jenish C. Mehta , Leonard J. Schulman

Additive spanners are fundamental graph structures with wide applications in network design, graph sparsification, and distance approximation. In particular, a $4$-additive spanner is a subgraph that preserves all pairwise distances up to…

Data Structures and Algorithms · Computer Science 2025-10-21 Chuhan Qi

We give a stochastic optimization algorithm that solves a dense $n\times n$ real-valued linear system $Ax=b$, returning $\tilde x$ such that $\|A\tilde x-b\|\leq \epsilon\|b\|$ in time: $$\tilde O((n^2+nk^{\omega-1})\log1/\epsilon),$$ where…

Data Structures and Algorithms · Computer Science 2024-06-10 Michał Dereziński , Jiaming Yang

The integrability-based solution of string theories related to AdS(n)/CFT(n-1) dualities relies on the worldsheet S matrix. Using generalized unitarity we construct the terms with logarithmic dependence on external momenta at one- and…

High Energy Physics - Theory · Physics 2014-05-28 Oluf Tang Engelund , Ryan W. McKeown , Radu Roiban

This paper presents an algorithmic method for generating random orthogonal matrices \(A\) that satisfy the property \(A^t S A = S\), where \(S\) is a fixed real invertible symmetric or skew-symmetric matrix. This method is significant as it…

Numerical Analysis · Mathematics 2024-12-19 Ali Saraeb

Matrix sensing has many real-world applications in science and engineering, such as system control, distance embedding, and computer vision. The goal of matrix sensing is to recover a matrix $A_\star \in \mathbb{R}^{n \times n}$, based on a…

Data Structures and Algorithms · Computer Science 2023-03-23 Lianke Qin , Zhao Song , Ruizhe Zhang

While semidefinite programming (SDP) problems are polynomially solvable in theory, it is often difficult to solve large SDP instances in practice. One technique to address this issue is to relax the global positive-semidefiniteness (PSD)…

Optimization and Control · Mathematics 2020-02-11 Grigoriy Blekherman , Santanu S. Dey , Marco Molinaro , Shengding Sun

Motivated by recent progress on stochastic matching with few queries, we embark on a systematic study of the sparsification of stochastic packing problems (SPP) more generally. Specifically, we consider SPPs where elements are independently…

Data Structures and Algorithms · Computer Science 2022-11-16 Shaddin Dughmi , Yusuf Hakan Kalayci , Neel Patel

The Symmetric Primal-Dual Symplex Pivot Decision Strategy (spdspds) is a novel iterative algorithm to solve linear programming problems. A symplex pivoting operation is simply an exchange between a basic variable and a non-basic variable,…

Optimization and Control · Mathematics 2026-05-19 Keshava Prasad Halemane

Given a DNF formula on n variables, the two natural size measures are the number of terms or size s(f), and the maximum width of a term w(f). It is folklore that short DNF formulas can be made narrow. We prove a converse, showing that…

Computational Complexity · Computer Science 2012-05-17 Parikshit Gopala , Raghu Meka , Omer Reingold

We consider the problem of deciding if a set of quantum one-qudit gates $\mathcal{S}=\{g_1,\ldots,g_n\}\subset G$ is universal, i.e if the closure $\overline{<\mathcal{S}>}$ is equal to $G$, where $G$ is either the special unitary or the…

Quantum Physics · Physics 2017-11-07 Adam Sawicki , Katarzyna Karnas

Let $\mathcal A$ be the adjacency matrix of a random $d$-regular graph on $N$ vertices, and we denote its eigenvalues by $\lambda_1\geq \lambda_2\cdots \geq \lambda_{N}$. For $N^{2/3}\ll d\leq N/2$, we prove optimal rigidity estimates of…

Probability · Mathematics 2024-08-01 Yukun He

Global polynomial optimization is an important tool across applied mathematics, with many applications in operations research, engineering, and physical sciences. In various settings, the polynomials depend on external parameters that may…

Optimization and Control · Mathematics 2024-06-14 Richard L. Zhu , Mathias Oster , Yuehaw Khoo
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