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Low Rank Approximation

This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local;

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Low Rank and Sparse Modeling for Visual Analysis

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding;

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Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation;

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Low-Rank Models in Visual Analysis

Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their;

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Fundamentals of Matrix Computations

decomposition based on adaptive over-complete dictionary, lower bounds for the low-rank matrix approximation and a semi-smoothing augmented lagrange;

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Low-rank Coals for Power Generation, Fuel and Chemical Production

Low-Rank Coals for Power Generation, Fuel and Chemical Production provides a thorough introduction to lignite (brown coal) and;

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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem;

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Tensor Numerical Methods in Scientific Computing

approximation of multivariate functions and operators by using the appropriate tensor formats. The old and new rank-structured tensor formats are;

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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace;

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Approximation Theory XVI

papers on a variety of topics in approximation theory, including Balian-Low theorems, butterfly spline interpolation, cubature rules, Hankel and;

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Turbo Message Passing Algorithms for Structured Signal Recovery

signals include 1) a sparse vector/matrix (which corresponds to the compressed sensing (CS) problem), 2) a low-rank matrix (which corresponds to;

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Computational Methods for Approximation of Large-Scale Dynamical Systems

and comparing the various MOR techniques for standard systems. The book also discusses the low-rank alternating direction implicit (LR-ADI;

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Low cycle fatigue: Analysis of a real case

This publication aims to provide to the designer a method for sizing, as first approximation, of upper mount cabin shock-absorber. It;

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Charge Sharing SAR ADCs for Low Voltage Low Power Applications

This book introduces readers to the potential of charge-sharing (CS) successive approximation register (SAR) analog-to-digital converters;

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Charge-Sharing SAR ADCs for Low-Voltage Low-Power Applications

This book introduces readers to the potential of charge-sharing (CS) successive approximation register (SAR) analog-to-digital converters;

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Asymptotic Statistics

, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes;

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Spectral Computations for Bounded Operators

must approximate such operators by finite rank operators, then solve the original eigenvalue problem approximately. Serving as both an;

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Approximation Theory and Algorithms for Data Analysis

This textbook offers an accessible introduction to the theory and numerics of approximation methods, combining classical topics;

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Tensor Numerical Methods in Quantum Chemistry

laptop using 3D uniform tensor grids of the size up to 1015. Fast tensor-based Hartree-Fock solver, incorporating the grid-based low-rank;

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Approximation Theory

Contains the contributions of 45 internationally distinguished mathematicians covering all areas of approximation theory-written in honor;

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New Trends in Approximation Theory

The international conference entitled New Trends in Approximation Theory was held at the Fields Institute, in Toronto, from July 25 until;

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Gradient Type Methods for Large Scale Optimization

) method that is a famous two-point stepsize gradient method. Due to BB method's simplicity, low storage and numerical efficiency, the BB method;

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Rational Approximation of Real Functions

Originally published in 1987, this book is devoted to the approximation of real functions by real rational functions. These are, in many;

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Rational Approximation of Real Functions

Originally published in 1987, this book is devoted to the approximation of real functions by real rational functions. These are, in many;

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Sparse Approximation with Bases

This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous;

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Leise Schwimmt Der Mond W/Hamann, Zelle, Rank W/HAMANN, ZELLE, RANK. Rank, CD

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