a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

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1 Math E-2b Lecture #8 Notes This week is all about determiats. We ll discuss how to defie them, how to calculate them, lear the allimportat property kow as multiliearity, ad show that a square matrix A is ivertible if ad oly if its determiat is ozero. We ll also derive some useful geometric applicatios that will allow us to ot oly calculate legth, area, ad volume, but also to defie geometric cotet (k-volume) i higher dimesios. We will also give a iterpretatio of the determiat as a expasio factor for geometric cotet. We ll wrap it up with a few mior results (Cramer s Rule ad a ot-too-practical formula for the iverse of a matrix). Defiig the determiat You are probably already familiar with the determiat i the case of 2 2 ad perhaps 3 3 matrices. Let s start with those ad reverse egieer the geeral defiitio for ay square matrix. matrix: Just for the sake of cosistecy, let s defie det[ a] a for a matrix. a b a b 2 2 matrix: We defie det ad bc c d. c d 3 3 matrix: We defie a a a a a a a a a a a a det a a a a a a a a a a a a a a a a ( a a a a ) a ( a a a a ) a ( a a a a ) a32 a33 a3 a33 a3 a a a a a a a a a a a a a a a a a a a This defiitio is based o a fact that we have ot yet established called the Laplace expasio, but let s take this as give ad see what, if ay, patter it suggests. Note that there is ust term for the determiat of a matrix, 2 terms for a 2 2 matrix (oe positive, oe egative), ad 3! 6 terms for a 3 3 matrix (half of them positive ad half egative). Also ote that the umber of factors i each term grows with the size of the matrix. A more subtle observatio is that, at least as writte for the 3 3 case, all terms are of the form ax a2ya 3z ad the choices of x, yz, correspod precisely with the differet ways of permutig the characters i 23, i.e. 23,32, 23, 23,32,32. Fially, ote that the sig of each term correspods to whether this is a eve permutatio (positive if obtaied by a eve umber of traspositios of the characters startig with 23) or a odd permutatio (egative if obtaied by a eve umber of traspositios). Based o these observatios, we might (correctly) speculate that for a matrix we should defie the determiat as follows: a a Defiitio: Give a matrix A, we defie det A sg( ) a () a2 (2) a ( ) where P ( ) a a P ( ) deotes the set of all permutatios of the characters 2 ; deotig a idividual permutatio; ( i) deotig where the character i is mapped uder that permutatio; ad sg( ) if is a eve permutatio ad sg( ) if is a odd permutatio. There will be! terms i the sum correspodig to the umber of permutatios i P. ( ) There are other ways to defie the determiat, but this is a practical defiitio at least i the case of relatively small matrices. Two simple observatios ) If A is either upper triagular or lower triagular, all but oe of the terms i the determiat will vaish ad the determiat will be simply the product of its diagoal etries. T 2) For ay matrix, det A det A. [The sum is the same, ust rearraged ad with the same sigs.] revised 4/4/203

2 Multiliearity Note that the determiat is, i fact, a fuctio det : R R that takes ay matrix A ad yields the real umber det A. As a fuctio from oe liear space to aother, the determiat is ot liear. For example, if we a b at bt were to scale a 2 2 matrix A c d (with det A ad bc ), we have ta ct dt ad det( ta) t ad t bc t ( ad bc) t det A. More geerally, for ay matrix, we have det( ta) t det A. However, the determiat is liear i ay sigle row or colum. This is kow as multiliearity. example: det 3x 2x x x 2 2 x 2 x 2 2 x x x x x x x x x x x. example: det (4 2 ) 2(3 2 ) (3 4 ) x x2 x3 x3 The multiliearity property gives several immediate corollaries. I terms of the kth colum of a matrix: det v xy v det v x v det v y v ad det v rx v rdet v x v I terms of the kth row of a matrix: v v v det xy det x det y v v v v v ad det rx rdet x v v This actually explais the Laplace expasio. Choose ay row or colum of the matrix A ad for each etry ai of that row or colum, let A i be its mior the ( ) ( ) matrix obtaied by deletig the ith row ad th colum of the matrix A. The, i terms of the ith row, i det A ( ) a det A ; ad i terms of the i i th colum, i det A ( ) a det A. i i i 2 revised 4/4/203

3 a a For example, i terms of the st row of a matrix A, we ca express a a a a a 0 a 0. Applyig liearity i the st row, this gives; a a 0 0 det A a det a det ad because of all the 0 s i the first a a a a a a row of each, ad some observatios about eve vs. odd permutatios to determie the sigs, this becomes a a a22 a2 a2 a2 det A a det ( ) a det a a a2 a a a a det A ( ) adet A The same idea applies to ay choice of row or colum with appropriate sigs. 3 2 Example: If A 0, we ca choose to expad alog ay row or colum. We ofte choose a row with 3 oe or more 0 s i order to miimize the umber of ozero terms i the sum, but ot ecessarily. 0 0 Expadig alog the st row gives det A 3 2 3(3) 2(3) (2) Expadig alog the 2d row gives det A 0 (5) (0) 0( ) Expadig alog the 3rd colum gives det A 0 3 (2) 0( ) 3() 5. Effect of elemetary row operatios o the determiat For ay matrix A, we have the followig properties: (a) A B det Bk det A by scale row k AB det B det A scale row by k, k0 iterchage (b) AB det Bdet A two rows add a multiple of (c) AB det Bdet A oe row to aother k Property (a) follows directly from liearity i ay oe row. Property (b) follows by observig that all the terms i the determiat will be the same except that eve permutatios will become odd ad vice-versa. This causes all the sigs to be reversed. Property (b) also implies that if a matrix has two idetical rows, the its determiat must be zero. Property (c) requires a small argumet for ustificatio: vi vi vi v i vi det det kdet det k0 det v kvi v v i v v 3 revised 4/4/203

4 There are at least two sigificat results that flow from these observatios. The first has to do with simplificatio of the calculatio of a determiat by first doig some row reductio. The secod will give a ew criterio for ivertibility of a matrix. We ca calculate the determiat of a matrix by double trackig the steps i row reductio ad the effect of each step o the value of the determiat. This is especially useful for larger matrices. Example: Calculate det A for the matrix Solutio: 2 3 A det A det A det A det A 33 det A 33 det A We could coclude from the 4th etry whe we obtaied a upper triagular matrix that det A 33, so det A 33. We could also have completed the row reductio to get to reduced row-echelo form. This would give that 33 det A, so det A 33. Ivertibility ad the determiat Suppose we bega with a matrix A ad carried out a sequece of steps to obtai rref( A ). This sequece of steps would ivolve s row swaps which would affect the determiat by multiplyig by ( ) s, r row scaligs by factors,,, (where k k k2 k, k2,, kr 0), ad some umber of steps where a multiple of a pivot row is r added to aother row. The effect of these row operatios o the determiat the gives that s det[rref ( )] ( ) s A det( A). From this we coclude that det( A) ( ) kk 2 k r det[rref ( A)]. k k 2 k r There are oly two possible values for det[rref ( A )]. If the matrix A is ivertible with rak, the rref ( A) I ad det[rref ( A )]. If the matrix A is ot ivertible with rak k, the rref ( A ) will have at least oe allzero row ad det[rref ( A )] 0. From the result above, this gives the followig importat theorem: Theorem: A matrix A is ivertible if ad oly if det A 0. There are a umber of other facts about determiats of both practical ad theoretical value. Propositio: If A ad B are matrices, the det( AB) (det A)(det B ). Proof: If the matrix A is ot ivertible, the AB will also ot be ivertible ad det A 0 ad det( AB ) 0, so the result holds i this case. A homework exercise shows that i the case where A is ivertible ad B is a arbitrary matrix, the rref[ A AB] [ I B ]. If the row reductio from A to I ivolves the same row operatios as outlied previously, the these same row operatios would be applied i reducig AB to B, so s det( AB) ( ) kk 2 k r det( B) det( A)det( B). Propositio: If A is ivertible, the Proof: If A is ivertible, the are reciprocals. A A det( A ) det( A ). I, so det( ) det( )det( ) det( ) A A A A I, so Propositio: If two matrices A ad B are similar, the det A det B. det( ) A ad det( A ) 4 revised 4/4/203

5 Proof: Two matrices A ad B are similar if a oly if B S AS for some ivertible (chage of basis) matrix S. Therefore det det( B S AS) det( S )det( A)det( S) det( A ). This last propositio yields a importat corollary: Corollary: Suppose V is a fiite-dimesioal vector space ad T : V V is a liear trasformatio. The the determiat det( T ) is well-defied. That is, if B is ay basis for V ad if A T is the matrix of T relative to B this basis, ad if we defie det( T ) det( A ), the this value will be the same o matter what basis we choose. Proof: If we choose ay other basis the the matrix of T relative to this other basis will be B S AS for some ivertible (chage of basis) matrix S. Therefore det( T ) det A det B from the previous propositio. Geometry ad the determiat If we merge some of the previous iformatio about Gram-Schmidt orthogoalizatio ad QR factorizatio with the curret facts about determiats, we ca derive some importat ad useful results. Recall that if v,, vk are liearly idepedet ad if we write A v v2 vk, the the Gram-Schmidt process r v2 u vk u 0 r22 k 2 gave v u A v v2 vk u u2 uk QR. 0 0 rkk k matrix w/liearly k matrix idepedet colums w/orthoormal colums kk upper triagular matrix with ozero diagoal etries The colums of the matrix A are the origial vectors; the colums of the matrix Q are those of the Gram- Schmidt basis; ad the etries of the matrix R capture all of the geometric aspects of the origial basis, i.e. legths, areas, etc. ad the o-orthogoality of the origial vectors. The k-volume of the parallelepiped determied by v,, vk is ust the product of the diagoal etries of R, i.e. rr 22 rkk det R. T T T T T T Note that with A QR we have A A ( QR) QR R Q QR R IkR R R. Therefore det( ) det( ) det( )det( ) det( )det( ) (det ) ( -volume) T T T 2 2 AA RR R R R R R k, so This is a very hady way to calculate areas, volumes, ad their higher-dimesioal aalogues. Example: I 3 R, fid the area of the parallelogram determied by the vectors T k-volume det( AA ). v 2 ad v Solutio: I multivariable calculus, we would likely fid the area of this parallelogram usig the cross product. 4 We would calculate that vv 2 5 ad fid its magitude: Area v v Usig our determiat method, we write 2 0 T A ad calculate AA So T 4 5 T det( AA ) det ad Area 2-volume det( AA ) It is importat to ote that the cross product is oly defied i very limited applicability. 3 R, so ay method ivolvig cross products has 5 revised 4/4/203

6 Special Case: Determiat of a matrix as a expasio factor If T T 2 2 A v v is a matrix, the det( AA) det( A)det( A) (det A ) ( -volume) ad the - volume determied by the vectors,, T 2 v v is give by det( AA) (det A) det A. If we further ote v Ae that v Ae determied by v,, v, we ca observe that the uit -cube determied by e e,, is mapped to the parallelepiped, so the volume is expaded from to det A. This result exteds to ay regio i the domai ad eables us to thik of det A as a volume expasio factor. This provides a simple geometric iterpretatio of the fact that det( AB) (det A)(det B ) (ad therefore det( AB) det A det B ). Sice the product of two matrices correspods to the compositio of liear trasformatios, ad if oe scales volume by det B ad this is followed by a trasformatio that scales volume by det A, the the compositio should scale volume by the product det A det B. It s ot hard to reaso that the sig of the determiat will be positive if the liear trasformatio is orietatio preservig ad egative if the trasformatio is orietatio reversig. Ideed, we ca defie these terms by the sig of the determiat. Cramer s Rule Cookbook recipe for fidig the iverse of a ivertible matrix Notes by Robert Witers 6 revised 4/4/203