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In contrast to the many continuous global optimization methods that assume the objective function and constraints are factorable, we study how to find globally maximal solutions to problems that are not factorable, focusing on a particular…

Optimization and Control · Mathematics 2022-08-31 Hugh Medal , Izuwa Ahanor

Calculations of collinear correlation functions in perturbative QCD and Soft-Collinear Effective Theory (SCET) require a prescription for subtracting soft or zero-bin contributions in order to avoid double counting the contributions from…

High Energy Physics - Phenomenology · Physics 2008-11-26 Ahmad Idilbi , Thomas Mehen

Next-to-leading order correction to the one-particle inclusive cross section in the framework of high energy factorization is calculated. Numerical results for midrapidity region are compared with predictions of conventional calculations…

High Energy Physics - Phenomenology · Physics 2014-11-17 Dmitry Ostrovsky

We consider the factorization of a rectangular matrix $X $ into a positive linear combination of rank-one factors of the form $u v^\top$, where $u$ and $v$ belongs to certain sets $\mathcal{U}$ and $\mathcal{V}$, that may encode specific…

Machine Learning · Computer Science 2013-09-13 Francis Bach

This text investigates relations between two well-known family of algorithms, matrix factorisations and recursive linear filters, by describing a probabilistic model in which approximate inference corresponds to a matrix factorisation…

Machine Learning · Statistics 2015-09-08 Ömer Deniz Akyıldız

In this paper, we propose an online algorithm to compute matrix factorizations. Proposed algorithm updates the dictionary matrix and associated coefficients using a single observation at each time. The algorithm performs low-rank updates to…

Machine Learning · Statistics 2015-06-29 Ömer Deniz Akyıldız

Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint. Here the goal is to extract a small size summary of the…

Data Structures and Algorithms · Computer Science 2025-05-26 Paul Dütting , Federico Fusco , Silvio Lattanzi , Ashkan Norouzi-Fard , Morteza Zadimoghaddam

Nonnegative matrix factorization is a powerful technique to realize dimension reduction and pattern recognition through single-layer data representation learning. Deep learning, however, with its carefully designed hierarchical structure,…

Computer Vision and Pattern Recognition · Computer Science 2017-07-31 Zhenxing Guo , Shihua Zhang

Matrix factorization is a well-studied task in machine learning for compactly representing large, noisy data. In our approach, instead of using the traditional concept of matrix rank, we define a new notion of link-rank based on a…

Machine Learning · Statistics 2018-05-02 Pouya Pezeshkpour , Carlos Guestrin , Sameer Singh

This paper introduces the multidimensional butterfly factorization as a data-sparse representation of multidimensional kernel matrices that satisfy the complementary low-rank property. This factorization approximates such a kernel matrix of…

Numerical Analysis · Mathematics 2017-06-12 Yingzhou Li , Haizhao Yang , Lexing Ying

This survey describes probabilistic algorithms for linear algebra computations, such as factorizing matrices and solving linear systems. It focuses on techniques that have a proven track record for real-world problem instances. The paper…

Numerical Analysis · Mathematics 2021-03-17 Per-Gunnar Martinsson , Joel Tropp

Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features). We consider general linear measurement settings,…

Machine Learning · Statistics 2017-09-20 Jiali Mei , Yohann De Castro , Yannig Goude , Jean-Marc Azaïs , Georges Hébrail

Renormalization-group methods in soft-collinear effective theory are used to perform the resummation of large perturbative logarithms for deep-inelastic scattering in the threshold region x->1. The factorization theorem for the structure…

High Energy Physics - Phenomenology · Physics 2010-10-27 Thomas Becher , Matthias Neubert , Ben D. Pecjak

Branes and defects in topological Landau-Ginzburg models are described by matrix factorisations. We revisit the problem of deforming them and discuss various deformation methods as well as their relations. We have implemented these…

High Energy Physics - Theory · Physics 2012-06-28 Nils Carqueville , Laura Dowdy , Andreas Recknagel

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors. We first illustrate this…

Machine Learning · Statistics 2014-12-10 Nicolas Gillis

Factorization of string amplitudes is one way of constructing string interaction vertices. We show that correlation functions in string theory can be conveniently factorized using loop variables representing delta functionals. We illustrate…

High Energy Physics - Theory · Physics 2015-06-26 B. Sathiapalan

The fast assembling of stiffness and mass matrices is a key issue in isogeometric analysis, particularly if the spline degree is increased. We present two algorithms based on the idea of sum factorization, one for matrix assembling and one…

Numerical Analysis · Mathematics 2019-06-26 A. Bressan , S. Takacs

The factorization of amplitudes into hard, soft and collinear parts is known to be violated in situations where incoming particles are collinear to outgoing ones. This result was first derived by studying limits where non-collinear…

High Energy Physics - Phenomenology · Physics 2017-09-20 Matthew D. Schwartz , Kai Yan , Hua Xing Zhu

Explicit applications of factorization theorems for processes at hadron colliders near the hadronic endpoint have largely focused on simple final states with either no jets (e.g., Drell-Yan) or one inclusive jet (e.g., deep inelastic…

High Energy Physics - Phenomenology · Physics 2014-11-20 Christian W. Bauer , Nicholas Daniel Dunn , Andrew Hornig

The formalism of the reduced density matrix is pursued in both length and velocity gauges of the perturbation to the crystal Hamiltonian. The covariant derivative is introduced as a convenient representation of the position operator. This…