 Chapter 9: Circles and Parabolas
 Chapter 9: Circles and Parabolas
 Chapter 1: Lines in the Plane
 Chapter 1.1: Lines in the Plane
 Chapter 1.2: Functions
 Chapter 1.3: Graphs of Functions
 Chapter 1.4: Shifting, Reflecting, and Stretching Graphs
 Chapter 1.5: Combinations of Functions
 Chapter 1.6: Inverse Functions
 Chapter 1.7: Linear Models and Scatter Plots
 Chapter 10: Analytic Geometry in Three Dimensions
 Chapter 10.1: The ThreeDimensional Coordinate System
 Chapter 10.2: Vectors in Space
 Chapter 10.3: The Cross Product of Two Vectors
 Chapter 10.4: Lines and Planes in Space
 Chapter 11: Limits and an Introduction to Calculus
 Chapter 11.1: Introduction to Limits
 Chapter 11.2: Techniques for Evaluating Limits
 Chapter 11.3: The Tangent Line Problem
 Chapter 11.4: Limits at Infinity and Limits of Sequences
 Chapter 11.5: The Area Problem
 Chapter 2: Polynomial and Rational Functions
 Chapter 2.1: Quadratic Functions
 Chapter 2.2: Polynomial Functions of Higher Degree
 Chapter 2.3: Real Zeros of Polynomial Functions
 Chapter 2.4: Complex Numbers
 Chapter 2.5: The Fundamental Theorem of Algebra
 Chapter 2.6: Rational Functions and Asymptotes
 Chapter 2.7: Graphs of Rational Functions
 Chapter 2.8: Quadratic Models
 Chapter 3: Exponential and Logarithmic Functions
 Chapter 3.1: Exponential Functions and Their Graphs
 Chapter 3.2: Logarithmic Functions and Their Graphs
 Chapter 3.3: Properties of Logarithms
 Chapter 3.4: Solving Exponential and Logarithmic Equations
 Chapter 3.5: Exponential and Logarithmic Models
 Chapter 3.6: Nonlinear Models
 Chapter 4: Trigonometric Functions
 Chapter 4.1: Radian and Degree Measure
 Chapter 4.2: Trigonometric Functions: The Unit Circle
 Chapter 4.3: Right Triangle Trigonometry
 Chapter 4.4: Trigonometric Functions of Any Angle
 Chapter 4.5: Graphs of Sine and Cosine Functions
 Chapter 4.6: Graphs of Other Trigonometric Functions
 Chapter 4.7: Inverse Trigonometric Functions
 Chapter 4.8: Applications and Models
 Chapter 5: Analytic Trigonometry
 Chapter 5.1: Using Fundamental Identities
 Chapter 5.2: Verifying Trigonometric Identities
 Chapter 5.3: Solving Trigonometric Equations
 Chapter 5.4: Sum and Difference Formulas
 Chapter 5.5: MultipleAngle and ProducttoSum Formulas
 Chapter 6: Additional Topics in Trigonometry
 Chapter 6.1: Law of Sines
 Chapter 6.2: Law of Cosines
 Chapter 6.3: Vectors in the Plane
 Chapter 6.4: Vectors and Dot Products
 Chapter 6.5: Trigonometric Form of a Complex Number
 Chapter 7: Linear Systems and Matrices
 Chapter 7.1: Solving Systems of Equations
 Chapter 7.2: Systems of Linear Equations in Two Variables
 Chapter 7.3: Multivariable Linear Systems
 Chapter 7.4: Matrices and Systems of Equations
 Chapter 7.5: Operations with Matrices
 Chapter 7.6: The Inverse of a Square Matrix
 Chapter 7.7: The Determinant of a Square Matrix
 Chapter 7.8: Phase Shift; Sinusoidal Curve Fitting
 Chapter 8: Sequences, Series, and Probability
 Chapter 8.1: Sequences, Series, and Probability
 Chapter 8.2: Arithmetic Sequences and Partial Sums
 Chapter 8.3: Geometric Sequences and Series
 Chapter 8.4: Mathematical Induction
 Chapter 8.5: The Binomial Theorem
 Chapter 8.6: Counting Principles
 Chapter 8.7: Probability
 Chapter 9: Circles and Parabolas
 Chapter 9.1: Circles and Parabolas
 Chapter 9.2: Ellipses
 Chapter 9.3: Hyperbolas
 Chapter 9.4: Rotation and Systems of Quadratic Equations
 Chapter 9.5: Parametric Equations
 Chapter 9.6: Polar Coordinates
 Chapter 9.7: Graphs of Polar Equations
 Chapter 9.8: Polar Equations of Conics
Precalculus With Limits A Graphing Approach 5th Edition  Solutions by Chapter
Full solutions for Precalculus With Limits A Graphing Approach  5th Edition
ISBN: 9780618851522
Precalculus With Limits A Graphing Approach  5th Edition  Solutions by Chapter
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Basis for V.
Independent vectors VI, ... , v d whose linear combinations give each vector in V as v = CIVI + ... + CdVd. V has many bases, each basis gives unique c's. A vector space has many bases!

CayleyHamilton Theorem.
peA) = det(A  AI) has peA) = zero matrix.

Column picture of Ax = b.
The vector b becomes a combination of the columns of A. The system is solvable only when b is in the column space C (A).

Commuting matrices AB = BA.
If diagonalizable, they share n eigenvectors.

Ellipse (or ellipsoid) x T Ax = 1.
A must be positive definite; the axes of the ellipse are eigenvectors of A, with lengths 1/.JI. (For IIx II = 1 the vectors y = Ax lie on the ellipse IIA1 yll2 = Y T(AAT)1 Y = 1 displayed by eigshow; axis lengths ad

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.

Inverse matrix AI.
Square matrix with AI A = I and AAl = I. No inverse if det A = 0 and rank(A) < n and Ax = 0 for a nonzero vector x. The inverses of AB and AT are B1 AI and (AI)T. Cofactor formula (Al)ij = Cji! detA.

Iterative method.
A sequence of steps intended to approach the desired solution.

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

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 .

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

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).

Multiplier eij.
The pivot row j is multiplied by eij and subtracted from row i to eliminate the i, j entry: eij = (entry to eliminate) / (jth pivot).

Reflection matrix (Householder) Q = I 2uuT.
Unit vector u is reflected to Qu = u. All x intheplanemirroruTx = o have Qx = x. Notice QT = Q1 = Q.

Right inverse A+.
If A has full row rank m, then A+ = AT(AAT)l has AA+ = 1m.

Row space C (AT) = all combinations of rows of A.
Column vectors by convention.

Singular matrix A.
A square matrix that has no inverse: det(A) = o.

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

Triangle inequality II u + v II < II u II + II v II.
For matrix norms II A + B II < II A II + II B II·

Volume of box.
The rows (or the columns) of A generate a box with volume I det(A) I.