Concept

Sparse matrix

Related publications (226)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Wavelet Statistics of Sparse and Self-Similar Images

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan

It is well documented that natural images are compressible in wavelet bases and tend to exhibit fractal properties. In this paper, we investigate statistical models that mimic these behaviors. We then use our models to make predictions on the statistics of ...

Siam Publications2015

RANS and LES computations of the tip-leakage vortex for different gap widths

Mohamed Farhat, Matthieu Dreyer

In hydraulic turbines, the tip-leakage vortex is responsible for flow instabilities and for promoting erosion due to cavitation. To better understand the tip vortex flow, Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) computations a ...

Taylor & Francis2015

Model-based Sketching and Recovery with Expanders

Volkan Cevher, Luca Baldassarre, Bubacarr Bah

Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper conside ...

2014

Evaluating functions of positive-definite matrices using colored-noise thermostats

Michele Ceriotti, Michele Parrinello

Many applications in computational science require computing the elements of a function of a large matrix. A commonly used approach is based on the the evaluation of the eigenvalue decomposition, a task that, in general, involves a computing time that scal ...

Amer Physical Soc2014

MR Image Reconstruction from Generalized Projections

Daniel Gallichan

Purpose: Currently, the time required for image reconstruction is prohibitively long if data are acquired using multidimensional imaging trajectories that make use of multichannel systems equipped with nonlinear gradients. Methods are presented that reduce ...

Wiley-Blackwell2014

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014

A Unified Formulation of Gaussian Versus Sparse Stochastic Processes-Part I: Continuous-Domain Theory

Michaël Unser, Pouya Dehghani Tafti

We introduce a general distributional framework that results in a unifying description and characterization of a rich variety of continuous-time stochastic processes. The cornerstone of our approach is an innovation model that is driven by some generalized ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Structured Sparse Coding for Image Denoising or Pattern Detection

Pascal Frossard, Sofia Karygianni

Sparsity has been one of the major drives in signal processing in the last decade. Structured sparsity has also lately emerged as a way to enrich signal priors towards more meaningful and accurate representations. In this paper we propose a new structured ...

2014

On the finite section method for computing exponentials of doubly-infinite skew-Hermitian matrices

Meiyue Shao

Computing the exponential of large-scale skew-Hermitian matrices or parts thereof is frequently required in applications. In this work, we consider the task of extracting finite diagonal blocks from a doubly-infinite skew-Hermitian matrix. These matrices u ...

Elsevier Science Inc2014

On sums of graph eigenvalues

Joachim Stubbe

We use two variational techniques to prove upper bounds for sums of the lowest several eigenvalues of matrices associated with finite, simple, combinatorial graphs. These include estimates for the adjacency matrix of a graph and for both the standard combi ...

Elsevier Science Inc2014