The 2, regularized group sparse optimization: Lower bound theory, recovery bound and algorithmsGroup sparsityGlobal convergenceRecovery boundLower boundIRLSSignal recoveryIn this paper, we consider an unconstra
optimizationproblemarepresentedandreviewed.Syntheticand actualstarimagereconstructionexamplesarepresentedwhich demonstratethemethod’ssuperiorperformanceascompared withseveralstandardimagedeconvolutionmethods. I.INTRODUCTION LURinlongexposureastronomicalstarimagesmaybe
This is in contrast to prior works such as [12, 32] where the barycentric measures are selected once and for all. The theory and methods that we present are general and can be applied to any structured prediction problem with measure-valued outputs. Among the possible applications we may ...
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support. julia high-performance-computing differential-equations factorization nonlinear-equations sparse-matrix sparse-matrices newton-raphson steady-state br...
From Theory to Practice – Signal and Image Processing Applications Front Matter Pages 167-167 Download chapterPDF Sparsity-Seeking Methods in Signal Processing Michael Elad Pages 169-184 Image Deblurring – A Case Study Michael Elad Pages 185-200 ...
The key technical ingredient in our proof is Jacobi’s bialternant formula for Schur polynomials from the theory of symmetric polynomials. Our result about non-negativity of univariate sparse polynomials should help to understand the impact of sparsity-based approaches to optimization problems of ...
It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide...
Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate
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It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide...