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We consider the problem of recovering a $k$-sparse signal ${\mbox{$\beta$}}_0\in\mathbb{R}^p$ from noisy observations $\bf y={\bf X}\mbox{$\beta$}_0+{\bf w}\in\mathbb{R}^n$. One of the most popular approaches is the $l_1$-regularized least…

Computation · Statistics 2022-11-23 Hanwen Huang

Universality is a pillar of modern critical phenomena. The standard scenario is that the two-point correlation algebraically decreases with the distance $r$ as $g(r) \sim r^{2-d-\eta}$, with $d$ the spatial dimension and $\eta$ the…

Statistical Mechanics · Physics 2021-09-17 Minghui Hu , Youjin Deng , Jian-Ping Lv

Critical behavior of three-dimensional classical frustrated antiferromagnets with a collinear spin ordering and with an additional twofold degeneracy of the ground state is studied. We consider two lattice models, whose continuous limit…

Strongly Correlated Electrons · Physics 2018-11-06 A. O. Sorokin

We consider systems whose steady-states exhibit a nonequilibrium phase transition from an active state to one -among an infinite number- absorbing state, as some control parameter is varied across a threshold value. The pair contact…

Statistical Mechanics · Physics 2009-11-07 F. van Wijland

Critical phenomena on scale-free networks with a degree distribution $p_k \sim k^{-\lambda}$ exhibit rich finite-size effects due to its structural heterogeneity. We systematically study the finite-size scaling of percolation and identify…

Statistical Mechanics · Physics 2025-08-29 Xuewei Zhao , Liwenying Yang , Dan Peng , Run-Ran Liu , Ming Li

Sparse recovery is widely applied in many fields, since many signals or vectors can be sparsely represented under some frames or dictionaries. Most of fast algorithms at present are based on solving $l^0$ or $l^1$ minimization problems and…

Numerical Analysis · Mathematics 2019-03-06 Chong-Jun Li , Yi-Jun Zhong

A non-perturbative Renormalization Group approach is used to calculate scaling functions for an O(4) model in d=3 dimensions in the presence of an external symmetry-breaking field. These scaling functions are important for the analysis of…

High Energy Physics - Theory · Physics 2008-11-26 Jens Braun , Bertram Klein

Suppose ${\bf x}$ is any exactly $k$-sparse vector in $\mathbb{C}^{n}$. We present a class of phase measurement matrix $A$ in $\mathbb{C}^{m\times n}$, and a corresponding algorithm, called SUPER, that can resolve ${\bf x}$ up to a global…

Information Theory · Computer Science 2014-01-28 Sheng Cai , Mayank Bakshi , Sidharth Jaggi , Minghua Chen

Average-case analysis computes the complexity of an algorithm averaged over all possible inputs. Compared to worst-case analysis, it is more representative of the typical behavior of an algorithm, but remains largely unexplored in…

Optimization and Control · Mathematics 2021-10-05 Courtney Paquette , Bart van Merriënboer , Elliot Paquette , Fabian Pedregosa

We study minimum vertex cover problems on random \alpha-uniform hypergraphs using two different approaches, a replica method in statistical mechanics of random systems and a leaf removal algorithm. It is found that there exists a phase…

Disordered Systems and Neural Networks · Physics 2014-07-03 Satoshi Takabe , Koji Hukushima

Uncovering and understanding universal dynamics in matter far from equilibrium remains a key challenge. In this work, we identify a so far unrecognized form of universal behavior that emerges after a sudden symmetry-breaking quench at…

Quantum Physics · Physics 2026-05-11 Tobias Wiener , Laurin Brunner , Markus Heyl

An accelerated class of adaptive scheme of iterative thresholding algorithms is studied analytically and empirically. They are based on the feedback mechanism of the null space tuning techniques (NST+HT+FB). The main contribution of this…

Information Theory · Computer Science 2020-05-15 Ningning Han , Shidong Li , Zhanjie Song

This paper considers the projection-free sparse convex optimization problem for the vector domain and the matrix domain, which covers a large number of important applications in machine learning and data science. For the vector domain…

Quantum Physics · Physics 2025-07-14 Jianhao He , John C. S. Lui

This paper presents an extension of the classical agnostic PAC learning model in which learning problems are modelled not only by a Hypothesis Space $\mathcal{H}$, but also by a Learning Space $\mathbb{L}(\mathcal{H})$, which is a cover of…

Machine Learning · Statistics 2021-09-13 Diego Marcondes , Adilson Simonis , Junior Barrera

We study the Haldane-Hubbard model by exact Renormalization Group techniques. We construct the topological phase diagram, for weak interactions. We predict that many-body interactions induce a shift of the transition line. The presence of…

Strongly Correlated Electrons · Physics 2016-12-13 Alessandro Giuliani , Ian Jauslin , Vieri Mastropietro , Marcello Porta

In the area of sparse recovery, numerous researches hint that non-convex penalties might induce better sparsity than convex ones, but up until now those corresponding non-convex algorithms lack convergence guarantees from the initial…

Information Theory · Computer Science 2014-04-29 Laming Chen , Yuantao Gu

The hypothesis of critical failure relates the presence of an ultimate stability point in the structural constitutive equation of materials to a divergence of characteristic scales in the microscopic dynamics responsible for deformation.…

Statistical Mechanics · Physics 2018-05-16 Jordi Baró , Jörn Davidsen

At a quantum critical point, the universal scaling behavior of R\'enyi entanglement entropy is controlled by the universality class of the codimension-two R\'enyi (or conical) defects in the infrared theory. In this work we perform a…

Strongly Correlated Electrons · Physics 2026-05-04 Yanzhang Zhu , Zhe Wang , Meng Cheng , Zheng Yan

We explore universal critical behavior in models with two competing order parameters, and an O(N)+O(M) symmetry for dimensions $d \leq 3$. In d=3, there is always exactly one stable Renormalization Group fixed point, corresponding to…

Statistical Mechanics · Physics 2016-10-12 Julia Borchardt , Astrid Eichhorn

Estimating high-dimensional precision matrices is a fundamental problem in modern statistics, with the graphical lasso and its $\ell_1$-penalty being a standard approach for recovering sparsity patterns. However, many statistical models,…

Statistics Theory · Mathematics 2025-06-19 Piotr Graczyk , Bartosz Kołodziejek , Hideto Nakashima , Maciej Wilczyński
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