106 CHAPTER 3. DETERMINANTS

Since the blocks of small identity matrices do not overlap,

∑s

 0 0 0

0 Ips×ps0

0 0 0

 =

Ip1×p1

0. . .

0 Ipp×pp

 = I

and so

∑s

BisAsj =∑s

(0 Iri×ri 0

)B

 0

Ips×ps

0

( 0 Ips×ps0)A

 0

Iqj×qj

0



=(

0 Iri×ri 0)B∑s

 0

Ips×ps

0

( 0 Ips×ps0)A

 0

Iqj×qj

0



=(

0 Iri×ri 0)BIA

 0

Iqj×qj

0

 =(

0 Iri×ri 0)BA

 0

Iqj×qj

0

which equals the ijth block of BA. Hence the ijth block of BA equals the formal multipli-cation according to matrix multiplication,

∑sBisAsj . ■

Example 3.5.3 Let an n×n matrix have the form A =

(a b

c P

)where P is n−1×n−1.

Multiply it by B =

(p q

r Q

)where B is also an n× n matrix and Q is n− 1× n− 1.

You use block multiplication(a b

c P

)(p q

r Q

)=

(ap+ br aq+ bQ

pc+ Pr cq+ PQ

)Note that this all makes sense. For example, b = 1 × n − 1 and r = n − 1 × 1 so br is a1× 1. Similar considerations apply to the other blocks.

Here is an interesting and significant application of block multiplication. In this theorem,qM (t) denotes the characteristic polynomial, det (tI −M) . The zeros of this polynomial willbe shown later to be eigenvalues of the matrixM . First note that from block multiplication,for the following block matrices consisting of square blocks of an appropriate size,(

A 0

B C

)=

(A 0

B I

)(I 0

0 C

)so

det

(A 0

B C

)= det

(A 0

B I

)det

(I 0

0 C

)= det (A) det (C)

Theorem 3.5.4 Let A be an m×n matrix and let B be an n×m matrix for m ≤ n. Then

qBA (t) = tn−mqAB (t) ,

so the eigenvalues of BA and AB are the same including multiplicities except that BA hasn−m extra zero eigenvalues. Here qA (t) denotes the characteristic polynomial of the matrixA.

106 CHAPTER 3. DETERMINANTSSince the blocks of small identity matrices do not overlap,0 oO 0 Ip. xp1 0»— O Ip.xp, O | = - =1s 0 0 0 0 Ip» xDpand so0 0do Bis Asy = > ( 0 Ir,xr; 0 )B In. xps ( 0 In.xp, 9 )A Tq; xas s (0) (0)0 0= ( 0 Lx; 0 )By Tp.xps ( 0 Tn, xps 0 )A Ta; x4s (0) 00 0=(0 Inxn 0) BIA] Tyna, |= (0 nxn 0) BAY Taxa,0 0which equals the ij” block of BA. Hence the ij*” block of B.A equals the formal multipli-cation according to matrix multiplication, )°, BisAs;. ibo P ) where P isn—-1xn-1.Example 3.5.3 Let annxn matriz have the form A = (cPqMultiply it by B= (r) where B is also ann X n matrix and Q isn-—1xn-—-1.You use block multiplicationa b p qa \_[ ap+br aq+bQc P r Q) \ pe+Pr cq+PQNote that this all makes sense. For example, b = 1x n-—1andr=n-—1x1I1sobrisa1 x 1. Similar considerations apply to the other blocks.Here is an interesting and significant application of block multiplication. In this theorem,qm (t) denotes the characteristic polynomial, det (tl — M). The zeros of this polynomial willbe shown later to be eigenvalues of the matrix M. First note that from block multiplication,for the following block matrices consisting of square blocks of an appropriate size,4A 0\ (4 0)\(1 0).Bc) \eri\oc}”vei ( aa (§ Jae (| ») aa (ay anoBC Bod 0 CTheorem 3.5.4 Let A be anm xn matrix and let B be ann Xm matrix form <n. Then= tmqBa (t) gas (t),so the eigenvalues of BA and AB are the same including multiplicities except that BA hasn—m extra zero eigenvalues. Here qa (t) denotes the characteristic polynomial of the matrizA.