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Simple Processors for Executing Scalar/Vector/ Matrix Instructions

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Scalar, Vector, and Matrix Mathematics

material on scalar inequalities, geometry, combinatorics, series, integrals, and more. Now more comprehensive than ever, Scalar, Vector, and Matrix;

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Scalar, Vector, and Matrix Mathematics Theory, Facts, and Formulas

material on scalar inequalities, geometry, combinatorics, series, integrals, and more. Now more comprehensive than ever, Scalar, Vector, and Matrix;

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Symbolic Calculus for Semilinear Hyperbolic Progressing Waves

singularities of semilinear hyperbolic progressing waves. The authors deal generally with real matrix valued co-efficients and with real vector valued;

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An Introduction to Vectors, Vector Operators and Vector Analysis

Ideal for undergraduate and graduate students of science and engineering, this book covers fundamental concepts of vectors and their;

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Matrix Mathematics Theory, Facts, and Formulas Second Edition

When first published in 2005, Matrix Mathematics quickly became the essential reference book for users of matrices in all branches;

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Multivariate Normal Distribution, The

univariate case and moves progressively toward the vector and matrix cases. The approach used in the book is a gradual one, going from one scalar;

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Arm(R) Helium(TM) Technology M-Profile Vector Extension (MVE) for Arm(R) Cortex(R)-M Processors: Reference Book

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Vector Analysis

developed to the extent that the uses of the potential function, both scalar and vector, are fully illustrated. Moreover, the basic postulates;

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Hot Topics in Linear Algebra

with several unknowns are naturally represented using the formalism of matrices and vectors. So we arrive at the matrix algebra, etc. Linear;

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Qualitative Analysis of Set-Valued Differential Equations

principle of comparison of Chaplygin - Wazhewsky, developed for the scalar, vector and matrix-valued Lyapunov functions and the method of nonlinear;

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Linear Algebra with Applications

covered include Gauss-Jordan elimination, matrix operations, vectors, dot product, linear transformation, matrix transformations, vector spaces;

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Linear Algebra with Applications

covered include Gauss-Jordan elimination, matrix operations, vectors, dot product, linear transformation, matrix transformations, vector spaces;

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A Primer on Hardware Prefetching

Since the 1970's, microprocessor-based digital platforms have been riding Moore's law, allowing for doubling of density for the same area;

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A Primer on Hardware Prefetching

Since the 1970's, microprocessor-based digital platforms have been riding Moore's law, allowing for doubling of density for the same area;

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Vector Optimization with Infimum and Supremum

results are used to explain and to improve practically relevant algorithms for linear vector optimization problems.;

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The Theory of Matrices

items in a matrix are called its elements or entries. Two matrices can be added or subtracted element by element. The rule for matrix;

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Duper Scalar Processor

. The hardware support for exploiting instruction level parallelism is very small and has very little decision making power. Most recently;

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The Visualization of Second-Order Tensor Fields

of continuous scalar, vector, or second-order tensor fields extending across a n-dimensional space, with n less than 1. While research efforts aimed at;

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Research Paper Handbook

Provides instructions on narrowing a topic, using the correct format, and writing the actual text for research papers, and offers;

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Multiplier Convergent Series

If is a space of scalar-valued sequences, then a series j xj in a topological vector space X is -multiplier convergent if the series j;

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Linear Models

ideas. Elegant geometric arguments are also invoked as needed and a review of vector spaces and matrices is provided to make the treatment self;

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Generalized Sylvester Equations

presents a unified parametric approach for solving various types of GSEs. In an extremely neat and elegant matrix form, the book provides a;

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Generalized Sylvester Equations

Solutions presents a unified parametric approach for solving various types of GSEs. In an extremely neat and elegant matrix form, the book;

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Regularization, Optimization, Kernels, and Support Vector Machines

-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing;

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Linear Algebra and Matrix Analysis for Statistics

Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the;

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