 Chapter 1.1: Systems of linear Equations
 Chapter 1.2: Matrices
 Chapter 1.3: Matrix Multiplication
 Chapter 1.4: Algebraic Properties of Matrix Operations
 Chapter 1.5: Special Types of Matrices and Partitioned Matrices
 Chapter 1.6: Matrix Transformations
 Chapter 1.7: Computer Graphics (Optional)
 Chapter 1.8: Correlation Coefficient (Optional)
 Chapter 2.1: Echelon Form of a Matrix
 Chapter 2.2: Solving Linear Systems
 Chapter 2.3: Elementary Matrices; Finding AI
 Chapter 2.4: Equivalent Matrices
 Chapter 2.5: LUFactorization (Optional)
 Chapter 3.1: Definition
 Chapter 3.2: Properties of Determinants
 Chapter 3.3: Cofactor Expansion
 Chapter 3.4: Inverse of a Matrix
 Chapter 3.5: Other Applicotions of Determinants
 Chapter 3.6: Determinants from a Computational Pointof View
 Chapter 4.1: Vectors in the Plane and in 3Spoce
 Chapter 4.2: Vector Spaces
 Chapter 4.3: Subspaces
 Chapter 4.4: Span
 Chapter 4.5: linear Independence
 Chapter 4.6: Basis and Dimension
 Chapter 4.7: Homogeneous Systems
 Chapter 4.8: Coordinates and Isomorphisms
 Chapter 4.9: Coordinates and Isomorphisms
 Chapter 5.1: length and Diredion in R2 and R3
 Chapter 5.2: Cross Product in R3 (Optional)
 Chapter 5.3: Inner Product Spaces
 Chapter 5.4: GramSchmidt Process
 Chapter 5.5: Orthogonal Complements
 Chapter 5.6: leasl Squares (Optional)
 Chapter 6.1: Definition and Examples
 Chapter 6.2: Kernel and Range of a linear Transformation
 Chapter 6.3: Matrix of a linear Transformation
 Chapter 6.4: Matrix of a linear Transformation
 Chapter 6.5: Similarity
 Chapter 6.6: Introduction to Homogeneous Coordinates (Optional)
 Chapter 7.1: Eigenvalues and Eigenvectors
 Chapter 7.2: Diagonalization and Similar Matrices
 Chapter 8.1: Stable Age Distribution in a Population; Markov Processes
 Chapter 8.2: Spectral Decomposition and Singular Value Decomposition
 Chapter 8.3: Dominanl Eigenvalue and Principal Component Analysis
 Chapter 8.4: Differential Equations
 Chapter 8.5: Dynamical Systems
 Chapter 8.6: Real Quadratic Forms
 Chapter 8.7: Conic Sections
 Chapter 8.8: Quadric Surfaces
 Chapter Chapter 1: Linear Equations and Matrices
 Chapter Chapter 2: Solving linear Systems
 Chapter Chapter 3: Compute IAI for each of the followin g:
 Chapter Chapter 4: Real Vector Spaces
 Chapter Chapter 5: looe, Pmd,cI Space,
 Chapter Chapter 6: Li near Transformations and Matrices
 Chapter Chapter 7: Eigenvalues and Eigenvectors
Elementary Linear Algebra with Applications 9th Edition  Solutions by Chapter
Full solutions for Elementary Linear Algebra with Applications  9th Edition
ISBN: 9780471669593
Elementary Linear Algebra with Applications  9th Edition  Solutions by Chapter
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Adjacency matrix of a graph.
Square matrix with aij = 1 when there is an edge from node i to node j; otherwise aij = O. A = AT when edges go both ways (undirected). Adjacency matrix of a graph. Square matrix with aij = 1 when there is an edge from node i to node j; otherwise aij = O. A = AT when edges go both ways (undirected).

Cyclic shift
S. Permutation with S21 = 1, S32 = 1, ... , finally SIn = 1. Its eigenvalues are the nth roots e2lrik/n of 1; eigenvectors are columns of the Fourier matrix F.

Diagonalizable matrix A.
Must have n independent eigenvectors (in the columns of S; automatic with n different eigenvalues). Then SI AS = A = eigenvalue matrix.

Identity matrix I (or In).
Diagonal entries = 1, offdiagonal entries = 0.

Indefinite matrix.
A symmetric matrix with eigenvalues of both signs (+ and  ).

Linearly dependent VI, ... , Vn.
A combination other than all Ci = 0 gives L Ci Vi = O.

Lucas numbers
Ln = 2,J, 3, 4, ... satisfy Ln = L n l +Ln 2 = A1 +A~, with AI, A2 = (1 ± /5)/2 from the Fibonacci matrix U~]' Compare Lo = 2 with Fo = O.

Markov matrix M.
All mij > 0 and each column sum is 1. Largest eigenvalue A = 1. If mij > 0, the columns of Mk approach the steady state eigenvector M s = s > O.

Matrix multiplication AB.
The i, j entry of AB is (row i of A)·(column j of B) = L aikbkj. By columns: Column j of AB = A times column j of B. By rows: row i of A multiplies B. Columns times rows: AB = sum of (column k)(row k). All these equivalent definitions come from the rule that A B times x equals A times B x .

Multiplicities AM and G M.
The algebraic multiplicity A M of A is the number of times A appears as a root of det(A  AI) = O. The geometric multiplicity GM is the number of independent eigenvectors for A (= dimension of the eigenspace).

Outer product uv T
= column times row = rank one matrix.

Pascal matrix
Ps = pascal(n) = the symmetric matrix with binomial entries (i1~;2). Ps = PL Pu all contain Pascal's triangle with det = 1 (see Pascal in the index).

Pivot columns of A.
Columns that contain pivots after row reduction. These are not combinations of earlier columns. The pivot columns are a basis for the column space.

Pivot.
The diagonal entry (first nonzero) at the time when a row is used in elimination.

Similar matrices A and B.
Every B = MI AM has the same eigenvalues as A.

Solvable system Ax = b.
The right side b is in the column space of A.

Spectral Theorem A = QAQT.
Real symmetric A has real A'S and orthonormal q's.

Sum V + W of subs paces.
Space of all (v in V) + (w in W). Direct sum: V n W = to}.

Vector addition.
v + w = (VI + WI, ... , Vn + Wn ) = diagonal of parallelogram.

Vector space V.
Set of vectors such that all combinations cv + d w remain within V. Eight required rules are given in Section 3.1 for scalars c, d and vectors v, w.