 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 13359 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.

Circulant matrix C.
Constant diagonals wrap around as in cyclic shift S. Every circulant is Col + CIS + ... + Cn_lSn  l . Cx = convolution c * x. Eigenvectors in F.

Cofactor Cij.
Remove row i and column j; multiply the determinant by (I)i + j •

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).

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.

Eigenvalue A and eigenvector x.
Ax = AX with x#O so det(A  AI) = o.

Fast Fourier Transform (FFT).
A factorization of the Fourier matrix Fn into e = log2 n matrices Si times a permutation. Each Si needs only nl2 multiplications, so Fnx and Fn1c can be computed with ne/2 multiplications. Revolutionary.

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

Fundamental Theorem.
The nullspace N (A) and row space C (AT) are orthogonal complements in Rn(perpendicular from Ax = 0 with dimensions rand n  r). Applied to AT, the column space C(A) is the orthogonal complement of N(AT) in Rm.

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

Incidence matrix of a directed graph.
The m by n edgenode incidence matrix has a row for each edge (node i to node j), with entries 1 and 1 in columns i and j .

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

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 •

Plane (or hyperplane) in Rn.
Vectors x with aT x = O. Plane is perpendicular to a =1= O.

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.

Projection matrix P onto subspace S.
Projection p = P b is the closest point to b in S, error e = b  Pb is perpendicularto S. p 2 = P = pT, eigenvalues are 1 or 0, eigenvectors are in S or S...L. If columns of A = basis for S then P = A (AT A) 1 AT.

Rayleigh quotient q (x) = X T Ax I x T x for symmetric A: Amin < q (x) < Amax.
Those extremes are reached at the eigenvectors x for Amin(A) and Amax(A).

Reduced row echelon form R = rref(A).
Pivots = 1; zeros above and below pivots; the r nonzero rows of R give a basis for the row space of A.

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

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.

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