 Chapter 1: Trigonometric Functions
 Chapter 1.1: Angles
 Chapter 1.2: Angle Relationships and Similar Triangles
 Chapter 1.3: Trigonometric Functions
 Chapter 1.4: Using the Definitions of the Trigonometric Functions
 Chapter 2: Acute Angles and Right Triangles
 Chapter 2.1: Trigonometric Functions of Acute Angles
 Chapter 2.2: Trigonometric Functions of NonAcute Angles
 Chapter 2.3: Approximations of Trigonometric Function Values
 Chapter 2.4: Solutions and Applications of Right Triangles
 Chapter 2.5: Further Applications of Right Triangles
 Chapter 3: Radian Measure and the Unit Circle
 Chapter 3.1: Radian Measure
 Chapter 3.2: Applications of Radian Measure
 Chapter 3.3: The Unit Circle and Circular Functions
 Chapter 3.4: Linear and Angular Speed
 Chapter 4: Graphs of the Circular Functions
 Chapter 4.1: Graphs of the Sine and Cosine Functions
 Chapter 4.2: Translations of the Graphs of the Sine and Cosine Functions
 Chapter 4.3: Graphs of the Tangent and Cotangent Functions
 Chapter 4.4: Graphs of the Secant and Cosecant Functions
 Chapter 4.5: Harmonic Motion
 Chapter 5: Trigonometric Identities
 Chapter 5.1: Fundamental Identities
 Chapter 5.2: Verifying Trigonometric Identities
 Chapter 5.3: Sum and Difference Identities for Cosine
 Chapter 5.4: Sum and Difference Identities for Sine and Tangent
 Chapter 5.5: DoubleAngle Identities
 Chapter 5.6: HalfAngle Identities
 Chapter 6: Inverse Circular Functions and Trigonometric Equations
 Chapter 6.1: Inverse Circular Functions
 Chapter 6.2: Trigonometric Equations I
 Chapter 6.3: Trigonometric Equations II
 Chapter 6.4: Equations Involving Inverse Trigonometric Functions
 Chapter 7.1: Oblique Triangles and the Law of Sines
 Chapter 7.2: The Ambiguous Case of the Law of Sines
 Chapter 7.3: The Law of Cosines
 Chapter 7.4: Geometrically Defined Vectors and Applications
 Chapter 7.5: Algebraically Defined Vectors and the Dot Product
 Chapter 8: Complex Numbers, Polar Equations, and Parametric Equations
 Chapter 8.1: Complex Numbers
 Chapter 8.2: Trigonometric (Polar) Form of Complex Numbers
 Chapter 8.3: The Product and Quotient Theorems
 Chapter 8.4: De Moivres Theorem; Powers and Roots of Complex Numbers
 Chapter 8.5: Polar Equations and Graphs
 Chapter 8.6: Parametric Equations, Graphs, and Applications
Trigonometry 11th Edition  Solutions by Chapter
Full solutions for Trigonometry  11th Edition
ISBN: 9780134217437
Trigonometry  11th Edition  Solutions by Chapter
Get Full SolutionsThis expansive textbook survival guide covers the following chapters: 46. Trigonometry was written by and is associated to the ISBN: 9780134217437. This textbook survival guide was created for the textbook: Trigonometry, edition: 11. Since problems from 46 chapters in Trigonometry have been answered, more than 7747 students have viewed full stepbystep answer. The full stepbystep solution to problem in Trigonometry were answered by , our top Math solution expert on 03/19/18, 04:02PM.

Cholesky factorization
A = CTC = (L.J]))(L.J]))T for positive definite A.

Complex conjugate
z = a  ib for any complex number z = a + ib. Then zz = Iz12.

Exponential eAt = I + At + (At)2 12! + ...
has derivative AeAt; eAt u(O) solves u' = Au.

Factorization
A = L U. If elimination takes A to U without row exchanges, then the lower triangular L with multipliers eij (and eii = 1) brings U back to 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.

GramSchmidt orthogonalization A = QR.
Independent columns in A, orthonormal columns in Q. Each column q j of Q is a combination of the first j columns of A (and conversely, so R is upper triangular). Convention: diag(R) > o.

Matrix multiplication AB.
The i, j entry of AB is (row i of A)ยท(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).

Orthogonal matrix Q.
Square matrix with orthonormal columns, so QT = Ql. Preserves length and angles, IIQxll = IIxll and (QX)T(Qy) = xTy. AlllAI = 1, with orthogonal eigenvectors. Examples: Rotation, reflection, permutation.

Orthogonal subspaces.
Every v in V is orthogonal to every w in W.

Polar decomposition A = Q H.
Orthogonal Q times positive (semi)definite H.

Projection p = a(aTblaTa) onto the line through a.
P = aaT laTa has rank l.

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.

Schur complement S, D  C A } B.
Appears in block elimination on [~ g ].

Semidefinite matrix A.
(Positive) semidefinite: all x T Ax > 0, all A > 0; A = any RT R.

Simplex method for linear programming.
The minimum cost vector x * is found by moving from comer to lower cost comer along the edges of the feasible set (where the constraints Ax = b and x > 0 are satisfied). Minimum cost at a comer!

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

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