三． 逆矩陣及行列式(Revers and determinant of matrix)

1． 方陣的逆和行列式(Revers and determinant of square matrix)

若a是方陣，且為非奇異陣，則方程ax=I和 xa=I有相同的解X。X稱為a的逆矩陣，記做a-1，在MATLAB中用inv 函數(shù)來(lái)計(jì)算矩陣的逆。計(jì)算方陣的行列式則用det函數(shù)。

DET? ??Determinant.

DET(X) is the determinant of the square matrix X.

Use COND instead of DET to test for matrix singularity.

INV ???Matrix inverse.

INV(X) is the inverse of the square matrix X. A warning message is printed if X is badly scaled or nearly singular.

例：計(jì)算方陣的行列式和逆矩陣。

a=[3? -3? 1;-3? 5? -2;1? -2? 1];

b=[14? 13? 5; 5? 1? 12;6? 14? 5];

d1=det(a)

x1=inv(a)

d2=det(b)

x2=inv(b)

d1 =

1

x1 =

1.0000??? 1.0000??? 1.0000

1.0000??? 2.0000??? 3.0000

1.0000??? 3.0000??? 6.0000

d2 =

-1351

x2 =

0.1207?? -0.0037?? -0.1118

-0.0348?? -0.0296??? 0.1058

-0.0474??? 0.0873?? ?0.0377

2． 廣義逆矩陣(偽逆)(Generalized inverse matrix)

一般非方陣無(wú)逆矩陣和行列式，方程ax=I 和xa=I至少有一個(gè)無(wú)解，這種矩陣可以求得特殊的逆矩陣，成為廣義逆矩陣(generalized inverse matrix)(或偽逆 pseudoinverse)。矩陣amn存在廣義逆矩陣xnm，使得 ax=Imn， MATLAB用pinv函數(shù)來(lái)計(jì)算廣義逆矩陣。

例：計(jì)算廣義逆矩陣。

a=[8? 14; 1? 3; 9? 6]

x=pinv(a)

b=x*a

c=a*x

d=c*a ????%d=a*x*a=a

e=x*c???? %e=x*a*x=x

a =

8??? 14

1???? 3

9???? 6

x =

-0.0661?? -0.0402??? 0.1743

0.1045??? 0.0406?? -0.0974

b =

1.0000?? -0.0000

-0.0000??? 1.0000

c =

0.9334??? 0.2472??? 0.0317

0.2472??? 0.0817?? -0.1177

0.0317?? -0.1177??? 0.9849

d =

8.0000?? 14.0000

1.0000??? 3.0000

9.0000??? 6.0000

e =

-0.0661?? -0.0402??? 0.1743

0.1045??? 0.0406?? -0.0974

PINV ??Pseudoinverse.

X = PINV(A) produces a matrix X of the same dimensions as A' so that A*X*A = A, X*A*X = X and A*X and X*A are Hermitian. The computation is based on SVD(A) and any singular values less than a tolerance are treated as zero.

The default tolerance is MAX(SIZE(A)) * NORM(A) * EPS.

PINV(A,TOL) uses the tolerance TOL instead of the default.

四． 矩陣分解(Matrix decomposition)

MATLAB求解線性方程的過(guò)程基于三種分解法則：

(１)Cholesky分解，針對(duì)對(duì)稱正定矩陣；

(２)高斯消元法，? 針對(duì)一般矩陣；

(３)正交化，?? ???針對(duì)一般矩陣(行數(shù)≠列數(shù))

這三種分解運(yùn)算分別由chol, lu和 qr三個(gè)函數(shù)來(lái)分解.

1．???????? Cholesky分解(Cholesky Decomposition)

僅適用于對(duì)稱和上三角矩陣

例：cholesky分解。

a=pascal(6)

b=chol(a)

a =

1???? 1???? 1???? 1???? 1???? 1

1???? 2???? 3???? 4???? 5???? 6

1???? 3???? 6??? 10??? 15??? 21

1???? 4??? 10??? 20??? 35??? 56

1???? 5??? 15??? 35??? 70?? 126

1???? 6??? 21??? 56?? 126?? 252

b =

1???? 1???? 1???? 1???? 1???? 1

0???? 1???? 2???? 3???? 4???? 5

0???? 0???? 1???? 3???? 6??? 10

0???? 0???? 0???? 1???? 4??? 10

0???? 0???? 0???? 0???? 1???? 5

0???? 0???? 0???? 0???? 0???? 1

CHOL? ?Cholesky factorization.

CHOL(X) uses only the diagonal and upper triangle of X. The lower triangular is assumed to be the (complex conjugate) transpose of the upper.? If X is positive definite, then R = CHOL(X) produces an upper triangular R so that R'*R = X. If X is not positive definite, an error message is printed.

[R,p] = CHOL(X), with two output arguments, never produces an

error message.? If X is positive definite, then p is 0 and R is the same as above.?? But if X is not positive definite, then p is a positive integer.

When X is full, R is an upper triangular matrix of order q = p-1

so that R'*R = X(1:q,1:q). When X is sparse, R is an upper triangular matrix of size q-by-n so that the L-shaped region of the first q rows and first q columns of R'*R agree with those of X.

2． LU分解(LU factorization).

用lu函數(shù)完成LU分解，將矩陣分解為上、下兩個(gè)三角陣，其調(diào)用格式為：

[l,u]=lu(a) ?l代表下三角陣，u代表上三角陣。

例：

LU分解。

a=[47? 24? 22; 11? 44? 0;30? 38? 41]

[l,u]=lu(a)

a =

47??? 24??? 22

11??? 44???? 0

30??? 38??? 41

l =

1.0000???????? 0???????? 0

0.2340??? 1.0000???????? 0

0.6383??? 0.5909? ??1.0000

u =

47.0000?? 24.0000?? 22.0000

0?? 38.3830?? -5.1489

0???????? 0?? 30.0000

LU ????LU factorization.

[L,U] = LU(X) stores an upper triangular matrix in U and a "psychologically lower triangular matrix" (i.e. a product of lower triangular and permutation matrices) in L, so that X = L*U. X can be rectangular.

[L,U,P] = LU(X) returns unit lower triangular matrix L, upper triangular matrix U, and permutation matrix P so that? P*X = L*U.

3． QR分解(Orthogonal-triangular decomposition).

函數(shù)調(diào)用格式：[q,r]=qr(a), q代表正規(guī)正交矩陣，r代表三角形矩陣。原始陣a不必一定是方陣。如果矩陣a是m×n階的，則矩陣q是m×m階的，矩陣r是m×n階的。

例：QR分解.

A=[22? 46? 20? 20; 30? 36? 46? 44;39? 8? 45? 2];

[q,r]=qr(A)

q =

-0.4082?? -0.7209?? -0.5601

-0.5566?? -0.2898??? 0.7786

-0.7236??? 0.6296?? -0.2829

r =

-53.8981? -44.6027? -66.3289? -34.1014

0? -38.5564??? 0.5823? -25.9097

0???????? 0?? 11.8800?? 22.4896

QR ????Orthogonal-triangular decomposition.

[Q,R] = QR(A) produces an upper triangular matrix R of the same

dimension as A and a unitary matrix Q so that A = Q*R.

[Q,R,E] = QR(A) produces a permutation matrix E, an upper

triangular R and a unitary Q so that A*E = Q*R.? The column

permutation E is chosen so that abs(diag(R)) is decreasing.

[Q,R] = QR(A,0) produces the "economy size" decomposition. If A is m-by-n with m > n, then only the first n columns of Q are computed.

4. 特征值與特征矢量(Eigenvalues and eigenvectors).

MATLAB中使用函數(shù)eig計(jì)算特征值和 特征矢量，有兩種調(diào)用方法：

*e=eig(a), 其中e是包含特征值的矢量；

*[v,d]=eig(a), 其中v是一個(gè)與a相同的n×n階矩陣，它的每一列是矩陣a的一個(gè)特征值所對(duì)應(yīng)的特征矢量，d為對(duì)角陣，其對(duì)角元素即為矩陣a的特征值。

例：計(jì)算特征值和特征矢量。

a=[34? 25? 15; 18? 35? 9; 41? 21? 9]

e=eig(a)

[v,d]=eig(a)

a =

34??? 25??? 15

18??? 35???? 9

41??? 21???? 9

e =

68.5066

15.5122

-6.0187

v =

-0.6227?? -0.4409?? -0.3105

-0.4969??? 0.6786?? -0.0717

-0.6044?? -0.5875??? 0.9479

d =

68.5066???????? 0???????? 0

0?? 15.5122???????? 0

0???????? 0?? -6.0187

EIG? ??Eigenvalues and eigenvectors.

E = EIG(X) is a vector containing the eigenvalues of a square matrix X.

[V,D] = EIG(X) produces a diagonal matrix D of eigenvalues and a full matrix V whose columns are the corresponding eigenvectors so that X*V = V*D.

[V,D] = EIG(X,'nobalance') performs the computation with balancing

disabled, which sometimes gives more accurate results for certain

problems with unusual scaling. If X is symmetric, EIG(X,'nobalance')

is ignored since X is already balanced.

5. 奇異值分解.( Singular value decomposition).

如存在兩個(gè)矢量u,v及一常數(shù)c,使得矩陣A滿足：Av=cu,? A’u=cv

稱c為奇異值，稱u,v為奇異矢量。

將奇異值寫成對(duì)角方陣∑，而相對(duì)應(yīng)的奇異矢量作為列矢量則可寫成兩個(gè)正交矩陣U，V，使得： AV=U∑， A‘U=V∑? 因?yàn)閁，V正交，所以可得奇異值表達(dá)式：

A=U∑V’。

一個(gè)m行n列的矩陣A經(jīng)奇異值分解，可求得m行m列的U, m行n列的矩陣∑和n行n列的矩陣V.。

奇異值分解用svd函數(shù)實(shí)現(xiàn)，調(diào)用格式為；

[u,s,v]=svd(a)

SVD??? Singular value decomposition.

[U,S,V] = SVD(X) produces a diagonal matrix S, of the same dimension as X and with nonnegative diagonal elements in decreasing order, and unitary matrices U and V so that X = U*S*V'.

S = SVD(X) returns a vector containing the singular values.

[U,S,V] = SVD(X,0) produces the "economy size" decomposition. If X is m-by-n with m > n, then only the first n columns of U are computed and S is n-by-n.

例： 奇異值分解。

a=[8? 5; 7? 3;4? 6];

[u,s,v]=svd(a) ????????????% s為奇異值對(duì)角方陣

u =

-0.6841?? -0.1826?? -0.7061

-0.5407?? -0.5228??? 0.6591

-0.4895??? 0.8327??? 0.2589

s =

13.7649???????? 0

0??? 3.0865

0???????? 0

v =

-0.8148?? -0.5797

-0.5797??? 0.8148

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