 6.8.1E: Find the leastsquares line 0 + 1xthat best fits the data assuming ...
 6.8.2E: Suppose 5 out of 25 data points in a weighted leastsquares problem...
 6.8.3E: Fit a cubic trend function to the data in Example 2. The orthogonal...
 6.8.4E: To make a trend analysis of six evenly spaced data points, one can ...
 6.8.5E: In Exercises 5–14, the space is C[ with the inner product (6).Show ...
 6.8.6E: In Exercises 5–14, the space is C[ with the inner product (6).Show ...
 6.8.7E: In Exercises 5–14, the space is C[ with the inner product (6).Show ...
 6.8.8E: In Exercises 5–14, the space is C[ with the inner product (6).Find ...
 6.8.9E: In Exercises 5–14, the space is C[ with the inner product (6).Find ...
 6.8.10E: In Exercises 5–14, the space is C[ with the inner product (6).Find ...
 6.8.11E: In Exercises 5–14, the space is C[ with the inner product (6).Find ...
 6.8.12E: In Exercises 5–14, the space is C[ with the inner product (6).Find ...
 6.8.13E: In Exercises 5–14, the space is C[ with the inner product (6).Expla...
 6.8.14E: In Exercises 5–14, the space is C[ with the inner product (6).Suppo...
 6.8.15E: [M] Refer to the data in Exercise 13 in Section 6.6, concerning the...
 6.8.16E: [M] Let f4 and f5 be the fourthorder and fifthorder Fourier appro...
Solutions for Chapter 6.8: Linear Algebra and Its Applications 4th Edition
Full solutions for Linear Algebra and Its Applications  4th Edition
ISBN: 9780321385178
Solutions for Chapter 6.8
Get Full SolutionsChapter 6.8 includes 16 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 16 problems in chapter 6.8 have been answered, more than 32655 students have viewed full stepbystep solutions from this chapter. Linear Algebra and Its Applications was written by and is associated to the ISBN: 9780321385178. This textbook survival guide was created for the textbook: Linear Algebra and Its Applications, edition: 4.

Affine transformation
Tv = Av + Vo = linear transformation plus shift.

Determinant IAI = det(A).
Defined by det I = 1, sign reversal for row exchange, and linearity in each row. Then IAI = 0 when A is singular. Also IABI = IAIIBI and

Full column rank r = n.
Independent columns, N(A) = {O}, no free variables.

Hankel matrix H.
Constant along each antidiagonal; hij depends on i + j.

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

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

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.

Kirchhoff's Laws.
Current Law: net current (in minus out) is zero at each node. Voltage Law: Potential differences (voltage drops) add to zero around any closed loop.

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

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

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.

Nullspace matrix N.
The columns of N are the n  r special solutions to As = O.

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.

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

Random matrix rand(n) or randn(n).
MATLAB creates a matrix with random entries, uniformly distributed on [0 1] for rand and standard normal distribution for randn.

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).

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.

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!

Symmetric factorizations A = LDLT and A = QAQT.
Signs in A = signs in D.

Transpose matrix AT.
Entries AL = Ajj. AT is n by In, AT A is square, symmetric, positive semidefinite. The transposes of AB and AI are BT AT and (AT)I.