 Chapter 1: AN INTRODUCTION TO DATA AND FUNCTIONS
 Chapter 2: RATES OF CHANGE AND LINEAR FUNCTIONS
 Chapter 3: WHEN LINES MEET: LINEAR SYSTEMS
 Chapter 4: THE LAWS OF EXPONENTS AND LOGARITHMS: MEASURING THE UNIVERSE
 Chapter 5: GROWTH AND DECAY: AN INTRODUCTION TO EXPONENTIAL FUNCTIONS
 Chapter 6: LOGARITHMIC LINKS: LOGARITHMIC AND EXPONENTIAL FUNCTIONS
 Chapter 7: POWER FUNCTIONS
 Chapter 8: QUADRATICS AND THE MATHEMATICS OF MOTION
 Chapter 9: NEW FUNCTIONS FROM OLD
Explorations in College Algebra 5th Edition  Solutions by Chapter
Full solutions for Explorations in College Algebra  5th Edition
ISBN: 9780470466445
Explorations in College Algebra  5th Edition  Solutions by Chapter
Get Full SolutionsExplorations in College Algebra was written by Sieva Kozinsky and is associated to the ISBN: 9780470466445. This expansive textbook survival guide covers the following chapters: 9. This textbook survival guide was created for the textbook: Explorations in College Algebra, edition: 5. Since problems from 9 chapters in Explorations in College Algebra have been answered, more than 1555 students have viewed full stepbystep answer. The full stepbystep solution to problem in Explorations in College Algebra were answered by Sieva Kozinsky, our top Math solution expert on 12/23/17, 04:55PM.

Block matrix.
A matrix can be partitioned into matrix blocks, by cuts between rows and/or between columns. Block multiplication ofAB is allowed if the block shapes permit.

Companion matrix.
Put CI, ... ,Cn in row n and put n  1 ones just above the main diagonal. Then det(A  AI) = ±(CI + c2A + C3A 2 + .•. + cnA nl  An).

Dimension of vector space
dim(V) = number of vectors in any basis for V.

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.

Four Fundamental Subspaces C (A), N (A), C (AT), N (AT).
Use AT for complex A.

Fourier matrix F.
Entries Fjk = e21Cijk/n give orthogonal columns FT F = nI. Then y = Fe is the (inverse) Discrete Fourier Transform Y j = L cke21Cijk/n.

Hermitian matrix A H = AT = A.
Complex analog a j i = aU of a symmetric matrix.

Independent vectors VI, .. " vk.
No combination cl VI + ... + qVk = zero vector unless all ci = O. If the v's are the columns of A, the only solution to Ax = 0 is x = o.

Length II x II.
Square root of x T x (Pythagoras in n dimensions).

Linear combination cv + d w or L C jV j.
Vector addition and scalar multiplication.

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

Multiplication Ax
= Xl (column 1) + ... + xn(column n) = combination of columns.

Orthonormal vectors q 1 , ... , q n·
Dot products are q T q j = 0 if i =1= j and q T q i = 1. The matrix Q with these orthonormal columns has Q T Q = I. If m = n then Q T = Q 1 and q 1 ' ... , q n is an orthonormal basis for Rn : every v = L (v T q j )q j •

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

Partial pivoting.
In each column, choose the largest available pivot to control roundoff; all multipliers have leij I < 1. See condition number.

Particular solution x p.
Any solution to Ax = b; often x p has free variables = o.

Saddle point of I(x}, ... ,xn ).
A point where the first derivatives of I are zero and the second derivative matrix (a2 II aXi ax j = Hessian matrix) is indefinite.

Spectrum of A = the set of eigenvalues {A I, ... , An}.
Spectral radius = max of IAi I.

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

Wavelets Wjk(t).
Stretch and shift the time axis to create Wjk(t) = woo(2j t  k).
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