A Course in Linear Algebra with Applications"There are useful discussions of two nonstandard topics which caught my eye, the method of least squares and Markov processes, consistent with the author's concern for the applications and the expected readership, which render the text useful for business, economics and social science students as well as those in physical sciences and engineering ... the book has great value for self study as well as adoption as a classroom text ... By all means adopt Robinson's text and enjoy spreading the gospel of linear algebra." Frank B CannonitoUniversity of California, Irvine"... it is very carefully written, both from the point of view of mathematical content and style, and readability ... It should therefore be very suitable as a course book as well as for self-tuition." Mathematics Abstracts, Germany |
Contents
Chapter One Matrix Algebra | 1 |
Chapter Two Systems of Linear Equations | 35 |
Chapter Three Determinants | 65 |
Chapter Four Introduction to Vector Spaces | 101 |
Chapter Five Basis and Dimension | 131 |
Chapter Six Linear Transformations | 169 |
Other editions - View all
Course In Linear Algebra With Applications: Solutions To The Exercises Derek J S Robinson Limited preview - 1992 |
Common terms and phrases
arbitrary belongs bilinear form c₁ characteristic polynomial coefficient matrix column operations column space column vector compute coordinate vector d₁ defined denote det(A determinant diagonalizable dimension E₁ eigenvalues eigenvectors elementary matrices elementary row operations elements equals EXAMPLE EXERCISES f₁ form a basis function Hence inner product space integers invertible matrix Jordan normal form least squares solution line segments linear algebra linear combination linear operator linear system linear system AX linear transformation linearly independent minimum polynomial n x n matrix null space number of pivots obtain ordered basis orthonormal basis permutation Proof Prove quadratic form real numbers reduced row echelon represented respect row echelon form row space scalar multiplication skew-symmetric solve square matrix standard basis subset subspace Suppose systems of linear T₁ T₂ THEOREM u₁ U₂ unique upper triangular v₁ vector space X₁ Y₁ Y₂ zero vector