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We present numerical methods for computing the Schatten $p$-norm of positive semi-definite matrices. Our motivation stems from uncertainty quantification and optimal experimental design for inverse problems, where the Schatten $p$-norm…

Numerical Analysis · Mathematics 2020-05-21 Ethan Dudley , Arvind K. Saibaba , Alen Alexanderian

We study the complexity of computing the mixed Schatten $\|\Phi\|_{q\to p}$ norms of linear maps $\Phi$ between matrix spaces. When $\Phi$ is completely positive, we show that $\| \Phi \|_{q \to p}$ can be computed efficiently when $q \geq…

Quantum Physics · Physics 2026-01-26 Jan Kochanowski , Omar Fawzi , Cambyse Rouzé

In this paper we consider symmetric, positive semidefinite (SPSD) matrix $A$ and present two algorithms for computing the $p$-Schatten norm $\|A\|_p$. The first algorithm works for any SPSD matrix $A$. The second algorithm works for…

Data Structures and Algorithms · Computer Science 2018-08-08 Vladimir Braverman

We consider the twin problems of estimating the effective rank and the Schatten norms $\|{\bf A}\|_{s}$ of a rectangular $p\times q$ matrix ${\bf A}$ from noisy observations. When $s$ is an even integer, we introduce a polynomial-time…

Statistics Theory · Mathematics 2021-11-29 Solène Thépaut , Nicolas Verzelen

The matrix $p \rightarrow q$ norm is a fundamental quantity appearing in a variety of areas of mathematics. This quantity is known to be efficiently computable in only a few special cases. The best known algorithms for approximately…

Data Structures and Algorithms · Computer Science 2023-11-15 Larry Guth , Dominique Maldague , John Urschel

We give an efficient algorithm which can obtain a relative error approximation to the spectral norm of a matrix, combining the power iteration method with some techniques from matrix reconstruction which use random sampling.

Data Structures and Algorithms · Computer Science 2011-04-13 Malik Magdon-Ismail

We address the general mathematical problem of computing the inverse $p$-th root of a given matrix in an efficient way. A new method to construct iteration functions that allow calculating arbitrary $p$-th roots and their inverses of…

Rings and Algebras · Mathematics 2020-03-06 Dorothee Richters , Michael Lass , Andrea Walther , Christian Plessl , Thomas D. Kühne

The Schatten quasi-norm can be used to bridge the gap between the nuclear norm and rank function, and is the tighter approximation to matrix rank. However, most existing Schatten quasi-norm minimization (SQNM) algorithms, as well as for…

Information Theory · Computer Science 2016-11-29 Fanhua Shang , Yuanyuan Liu , James Cheng

The Schatten-$p$ norm ($0<p<1$) has been widely used to replace the nuclear norm for better approximating the rank function. However, existing methods are either 1) not scalable for large scale problems due to relying on singular value…

Machine Learning · Statistics 2016-11-28 Chen Xu , Zhouchen Lin , Hongbin Zha

We consider the quantum complexity of computing Schatten $p$-norms and related quantities, and find that the problem of estimating these quantities is closely related to the one clean qubit model of computation. We show that the problem of…

Quantum Physics · Physics 2017-06-29 Chris Cade , Ashley Montanaro

In this paper, we fully characterize the duality mapping over the space of matrices that are equipped with Schatten norms. Our approach is based on the analysis of the saturation of the H\"older inequality for Schatten norms. We prove in…

Functional Analysis · Mathematics 2020-09-17 Shayan Aziznejad , Michael Unser

A well-known approach in the design of efficient algorithms, called matrix sparsification, approximates a matrix $A$ with a sparse matrix $A'$. Achlioptas and McSherry [2007] initiated a long line of work on spectral-norm sparsification,…

Numerical Analysis · Mathematics 2023-09-12 Robert Krauthgamer , Shay Sapir

Given an $n \times d$ matrix $A$, its Schatten-$p$ norm, $p \geq 1$, is defined as $\|A\|_p = \left (\sum_{i=1}^{\textrm{rank}(A)}\sigma_i(A)^p \right )^{1/p}$, where $\sigma_i(A)$ is the $i$-th largest singular value of $A$. These norms…

Data Structures and Algorithms · Computer Science 2017-02-21 Yi Li , David P. Woodruff

This paper considers basic properties of super-operator norms induced by Schatten p-norms. Such super-operator norms arise in various contexts in the study of quantum information. It is proved that for completely positive super-operators,…

Quantum Physics · Physics 2007-05-23 John Watrous

We consider the problem of computing the q->p norm of a matrix A, which is defined for p,q \ge 1, as |A|_{q->p} = max_{x !=0 } |Ax|_p / |x|_q. This is in general a non-convex optimization problem, and is a natural generalization of the…

Data Structures and Algorithms · Computer Science 2010-05-04 Aditya Bhaskara , Aravindan Vijayaraghavan

In the recent years, the trace norm of graphs has been extensively studied under the name of graph energy. In this paper some of this research is extended to more general matrix norms, like the Schatten p-norms and the Ky Fan k-norms.…

Combinatorics · Mathematics 2010-08-05 Vladimir Nikiforov

We propose a numerical method, based on the shift-and-invert power iteration, that answers whether a symmetric matrix is positive definite ("yes") or not ("no"). Our method uses randomization. But, it returns the correct answer with high…

Numerical Analysis · Mathematics 2018-06-27 Martin Neuenhofen

King and Ruskai asked whether the $p\to q$ norm of a completely positive map $\Phi$, acting between Schatten $p$ and $q$ classes of self-adjoint operators, $||\Phi||_{p\to q} = \max_{A=A^*} \frac{||\Phi(A)||_q}{||A||_p}$, is equal to the…

Mathematical Physics · Physics 2013-04-23 Koenraad M. R. Audenaert

A linear map between matrix spaces is positive if it maps positive semidefinite matrices to positive semidefinite ones, and is called completely positive if all its ampliations are positive. In this article quantitative bounds on the…

Functional Analysis · Mathematics 2019-07-10 Igor Klep , Scott McCullough , Klemen Šivic , Aljaž Zalar

The Schatten-$p$ quasi-norm with $p\in(0,1)$ has recently gained considerable attention in various low-rank matrix estimation problems offering significant benefits over relevant convex heuristics such as the nuclear norm. However, due to…

Numerical Analysis · Mathematics 2020-10-28 Paris Giampouras , René Vidal , Athanasios Rontogiannis , Benjamin Haeffele
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