# Linear Algebra: Theory and Applications

@inproceedings{Cheney2010LinearAT, title={Linear Algebra: Theory and Applications}, author={Ward Cheney and David R. Kincaid}, year={2010} }

Motivated by their desire for a single text that meets the various requirements found in linear algebra courses, renowned writing team Ward Cheney and David Kincaid have developed this multi-faceted introduction to linear algebra. Focusing on a comprehensive blend between theory and application, this text guides theoretically-oriented mathematics students as they devise proofs and deal with abstractions. Likewise, this unique blend appeals to applications-oriented science and engineering…

## 112 Citations

Teaching the Singular Value Decomposition of Matrices: A Computational Approach

- Education
- 2012

One of the immediate conclusions is that without a good understanding of the fundamental concepts of linear algebra the topic of singular value decomposition of matrices could prove challenging for even the top achieving undergraduate students.

A Space Efficient Flexible Pivot Selection Approach to Evaluate Determinant and Inverse of a Matrix

- MathematicsPloS one
- 2014

These algorithms obviate the use of permutations and inverse permutations by taking the advantage of flexibility in pivot selection, one may easily avoid development of the fractions by most.

Orthogonal polynomials, repeated measures, and SPSS

- Mathematics
- 2017

ABSTRACT We discuss the construction of discrete orthonormal polynomials, using MAPLE procedures. We also study two important applications of these polynomials in statistics: in multiple linear…

Determinants of binomial-related circulant matrices

- MathematicsSpecial Matrices
- 2018

Abstract Due to their rich algebraic structures and wide applications, circulant matrices have been of interest and continuously studied. In this paper, n×n complex left and right circulant matrices…

Hermite Functions and Fourier Series

- MathematicsSymmetry
- 2021

The Gramm-Schmidt method permits the construction of orthonormal bases in the space of square integrable functions on the unit circle and in the Hilbert spaces, and operators come from multiplication and differentiation operators that, when applied to periodic functions, should preserve periodicity.

Supplement to “Unfolding-Model-Based Visualization: Theory, Method and Applications”

- Computer Science
- 2020

Following multidimensional unfolding, it is natural to bi-cluster the respondents and the items based on the estimated ideal points, using the Euclidian distance as a natural measure of dissimilarity.

Multivariate Approximation Methods Using Polynomial Models: A Comparative Study

- Computer Science2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)
- 2015

Three methods that allow us to express the solution to multivariate problems in a closed form: a) Fast Ascent (FA), b) Levenberg-Marquardt (LM) and c) Powell's Dog-Leg algorithms.

Orthogonal polynomials with respect to differential operators and matrix orthogonal polynomials

- Mathematics
- 2013

This thesis deals with the concept of orthogonal polynomials with respect to a differential operator, the study of the strong asymptotic behavior of eigenpolynomials of exactly solvable operators,…

HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries

- Computer ScienceSIGMOD Conference
- 2021

HADAD is proposed, an extensible lightweight approach for optimizing hybrid complex analytics queries, based on a common abstraction that facilitates unified reasoning: a relational model endowed with integrity constraints that can be naturally and portably applied on top of pure LA and hybrid RA-LA platforms without modifying their internals.

Vector space algebra for scaling and centering relationship matrices under non-Hardy–Weinberg equilibrium conditions

- Computer ScienceGenetics, selection, evolution : GSE
- 2021

Vector space methodology can be applied to measure orthogonality between vectors of additive and dominance scales and to construct alternative orthogonal models such as GSP-A, G SP-D and an orthonormal model such asGSP-N.

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