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When the linear measurements of an instance of low-rank matrix recovery satisfy a restricted isometry property (RIP)---i.e. they are approximately norm-preserving---the problem is known to contain no spurious local minima, so exact recovery…

Machine Learning · Computer Science 2018-11-01 Richard Y. Zhang , Cédric Josz , Somayeh Sojoudi , Javad Lavaei

For any finite point set in $D$-dimensional space equipped with the 1-norm, we present random linear embeddings to $k$-dimensional space, with a new metric, having the following properties. For any pair of points from the point set that are…

Probability · Mathematics 2020-11-09 Michael P. Casey

This paper deals with the computational complexity of conditions which guarantee that the NP-hard problem of finding the sparsest solution to an underdetermined linear system can be solved by efficient algorithms. In the literature, several…

Optimization and Control · Mathematics 2013-11-05 Andreas M. Tillmann , Marc E. Pfetsch

The restricted isometry property (RIP) is a well-known condition that guarantees the absence of spurious local minima in low-rank matrix recovery problems with linear measurements. In this paper, we introduce a novel property named bound…

Optimization and Control · Mathematics 2020-12-14 Yingjie Bi , Javad Lavaei

The expicit restricted isometry property (RIP) measurement matrices are needed in practical application of compressed sensing in signal processing. RIP matrices from Reed-Solomon codes, BCH codes, orthogonal codes, expander graphs have been…

Information Theory · Computer Science 2015-06-15 Liqing Xu , Hao Chen

A key technique of machine learning and computer vision is to embed discrete weighted graphs into continuous spaces for further downstream processing. Embedding discrete hierarchical structures in hyperbolic geometry has proven very…

Machine Learning · Computer Science 2023-08-17 Frank Nielsen , Ke Sun

We prove weak convergence in a separable Hilbert space for estimators of high-dimensional regression coefficients, which yields asymptotic normality and enables direct use of standard asymptotic tools such as the continuous mapping theorem.…

Statistics Theory · Mathematics 2026-05-05 Kou Fujimori , Koji Tsukuda

Restricted Isometry Property (RIP) is of fundamental importance in the theory of compressed sensing and forms the base of many exact and robust recovery guarantees in this field. A quantitative description of RIP involves bounding the…

Information Theory · Computer Science 2020-07-15 Gen Li , Xingyu Xu , Yuantao Gu

A matrix $A$ is said to have the $\ell_p$-Restricted Isometry Property ($\ell_p$-RIP) if for all vectors $x$ of up to some sparsity $k$, $\|{Ax}\|_p$ is roughly proportional to $\|{x}\|_p$. We study this property for $m \times n$ matrices…

Computational Complexity · Computer Science 2023-05-09 Venkatesan Guruswami , Peter Manohar , Jonathan Mosheiff

Let $\mathcal H$ be a complex Hilbert space and $\mathcal F_s (\mathcal H)$ the real vector space of all self-adjoint finite rank bounded operators on $\mathcal H$. We generalize the famous Wigner's theorem by characterizing linear maps on…

Functional Analysis · Mathematics 2026-04-17 Lucijan Plevnik

Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance…

Artificial Intelligence · Computer Science 2024-06-25 Arnaud Lequen , Martin C. Cooper , Frédéric Maris

Matrices $\Phi\in\R^{n\times p}$ satisfying the Restricted Isometry Property (RIP) are an important ingredient of the compressive sensing methods. While it is known that random matrices satisfy the RIP with high probability even for…

Probability · Mathematics 2018-11-19 David Gamarnik

The problem of computing minimally sparse solutions of under-determined linear systems is $NP$ hard in general. Subsets with extra properties, may allow efficient algorithms, most notably problems with the restricted isometry property (RIP)…

Machine Learning · Computer Science 2023-02-07 G. Welper

In this paper, we address the question of information preservation in ill-posed, non-linear inverse problems, assuming that the measured data is close to a low-dimensional model set. We provide necessary and sufficient conditions for the…

Information Theory · Computer Science 2018-12-05 Nicolas Keriven , Rémi Gribonval

We present an embedding of stochastic optimal control problems, of the so called path integral form, into reproducing kernel Hilbert spaces. Using consistent, sample based estimates of the embedding leads to a model free, non-parametric…

Machine Learning · Computer Science 2012-08-14 Konrad Rawlik , Marc Toussaint , Sethu Vijayakumar

This paper considers compressed sensing matrices and neighborliness of a centrally symmetric convex polytope generated by vectors $\pm X_1,...,\pm X_N\in\R^n$, ($N\ge n$). We introduce a class of random sampling matrices and show that they…

Probability · Mathematics 2009-05-01 Radosław Adamczak , Alexander E. Litvak , Alain Pajor , Nicole Tomczak-Jaegermann

We announce a generalization of Zimmer's cocycle superrigidity theorem proven using harmonic map techniques. This allows us to generalize many results concerning higher rank lattices to all lattices in semisimple groups with property $(T)$.…

Differential Geometry · Mathematics 2007-05-23 David Fisher , Theron Hitchman

We investigate harmonic maps in the context of isometric embeddings when the target space is Ricci-flat and has codimension one. With the help of the Campbell-Magaard theorem we show that any $n$-dimensional ($n\geqslant 3$) Lorentzian…

General Relativity and Quantum Cosmology · Physics 2015-06-25 S. Chervon , F. Dahia , C. Romero

Consider a rational map from a projective space to a product of projective spaces, induced by a collection of linear projections. Motivated by the the theory of limit linear series and Abel-Jacobi maps, we study the basic properties of the…

Algebraic Geometry · Mathematics 2013-11-01 Binglin Li

This paper is concerned with the question of reconstructing a vector in a finite-dimensional real or complex Hilbert space when only the magnitudes of the coefficients of the vector under a redundant linear map are known. We present new…

Functional Analysis · Mathematics 2012-07-06 Radu Balan