 Chapter 1: The Mathematics of Elections
 Chapter 10: Financial Mathematics
 Chapter 11: The Mathematics of Symmetry
 Chapter 12: Fractal Geometry
 Chapter 13: Fibonacci Numbers and the Golden Ratio
 Chapter 14: Censuses, Surveys, Polls, and Studies
 Chapter 15: Graphs, Charts, and Numbers
 Chapter 2: The Mathematics of Power
 Chapter 3: The Mathematics of Sharing
 Chapter 4: The Mathematics of Apportionment
 Chapter 5: The Mathematics of Getting Around
 Chapter 6: The Mathematics of Touring
 Chapter 7: The Mathematics of Networks
 Chapter 8: The Mathematics of Scheduling
 Chapter 9: Population Growth Models
Excursions in Modern Mathematics 8th Edition  Solutions by Chapter
Full solutions for Excursions in Modern Mathematics  8th Edition
ISBN: 9781292022048
Excursions in Modern Mathematics  8th Edition  Solutions by Chapter
Get Full SolutionsThe full stepbystep solution to problem in Excursions in Modern Mathematics were answered by , our top Math solution expert on 03/14/18, 04:56PM. This textbook survival guide was created for the textbook: Excursions in Modern Mathematics, edition: 8. This expansive textbook survival guide covers the following chapters: 15. Excursions in Modern Mathematics was written by and is associated to the ISBN: 9781292022048. Since problems from 15 chapters in Excursions in Modern Mathematics have been answered, more than 3129 students have viewed full stepbystep answer.

Characteristic equation det(A  AI) = O.
The n roots are the eigenvalues of A.

Condition number
cond(A) = c(A) = IIAIlIIAIII = amaxlamin. In Ax = b, the relative change Ilox III Ilx II is less than cond(A) times the relative change Ilob III lib II· Condition numbers measure the sensitivity of the output to change in the input.

Cross product u xv in R3:
Vector perpendicular to u and v, length Ilullllvlll sin el = area of parallelogram, u x v = "determinant" of [i j k; UI U2 U3; VI V2 V3].

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

Dot product = Inner product x T y = XI Y 1 + ... + Xn Yn.
Complex dot product is x T Y . Perpendicular vectors have x T y = O. (AB)ij = (row i of A)T(column j of B).

Echelon matrix U.
The first nonzero entry (the pivot) in each row comes in a later column than the pivot in the previous row. All zero rows come last.

Hessenberg matrix H.
Triangular matrix with one extra nonzero adjacent diagonal.

Hilbert matrix hilb(n).
Entries HU = 1/(i + j 1) = Jd X i 1 xj1dx. Positive definite but extremely small Amin and large condition number: H is illconditioned.

Kronecker product (tensor product) A ® B.
Blocks aij B, eigenvalues Ap(A)Aq(B).

Least squares solution X.
The vector x that minimizes the error lie 112 solves AT Ax = ATb. Then e = b  Ax is orthogonal to all columns of A.

Left nullspace N (AT).
Nullspace of AT = "left nullspace" of A because y T A = OT.

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.

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).

Positive definite matrix A.
Symmetric matrix with positive eigenvalues and positive pivots. Definition: x T Ax > 0 unless x = O. Then A = LDLT with diag(D» O.

Spanning set.
Combinations of VI, ... ,Vm fill the space. The columns of A span C (A)!

Standard basis for Rn.
Columns of n by n identity matrix (written i ,j ,k in R3).

Stiffness matrix
If x gives the movements of the nodes, K x gives the internal forces. K = ATe A where C has spring constants from Hooke's Law and Ax = stretching.

Toeplitz matrix.
Constant down each diagonal = timeinvariant (shiftinvariant) filter.

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