DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ

Size: px
Start display at page:

Download "DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ"

Transcription

1 DETERMINANTS Chpter 4 All Mthemticl truths re reltive nd conditionl. C.P. STEINMETZ 4. Introduction In the previous chpter, we hve studied bout mtrices nd lgebr of mtrices. We hve lso lernt tht sstem of lgebric equtions cn be expressed in the form of mtrices. This mens, sstem of liner equtions like x + b c x + b c b x c cn be represented s b c. Now, this sstem of equtions hs unique solution or not, is determined b the number b b. (Recll tht if b P.S. Lplce or, b b b 0, then the sstem of liner ( equtions hs unique solution. The number b b b which determines uniqueness of solution is ssocited with the mtrix A b nd is clled the determinnt of A or det A. Determinnts hve wide pplictions in Engineering, Science, Economics, Socil Science, etc. In this chpter, we shll stud determinnts up to order three onl with rel entries. Also, we will stud vrious properties of determinnts, minors, cofctors nd pplictions of determinnts in finding the re of tringle, djoint nd inverse of squre mtrix, consistenc nd inconsistenc of sstem of liner equtions nd solution of liner equtions in two or three vribles using inverse of mtrix. 4. Determinnt To ever squre mtrix A [ ij ] of order n, we cn ssocite number (rel or complex clled determinnt of the squre mtrix A, where ij (i, j th element of A.

2 04 MATHEMATICS This m be thought of s function which ssocites ech squre mtrix with unique number (rel or complex. If M is the set of squre mtrices, K is the set of numbers (rel or complex nd f : M K is defined b f(a k, where A M nd k K, then f(a is clled the determinnt of A. It is lso denoted b A or det A or. b If A c d Remrks, then determinnt of A is written s A b c d det (A (i For mtrix A, A is red s determinnt of A nd not modulus of A. (ii Onl squre mtrices hve determinnts. 4.. Determinnt of mtrix of order one Let A [] be the mtrix of order, then determinnt of A is defined to be equl to 4.. Determinnt of mtrix of order two Let A then the determinnt of A is defined s: be mtrix of order, det (A A Exmple Evlute 4. Solution We hve 4 ( 4( Exmple Evlute Solution We hve x x x + x x x x + x x (x (x + (x x (x x x Determinnt of mtrix of order 3 3 Determinnt of mtrix of order three cn be determined b expressing it in terms of second order determinnts. This is known s expnsion of determinnt long row (or column. There re six ws of expnding determinnt of order

3 DETERMINANTS 05 3 corresponding to ech of three rows (R, R nd R 3 nd three columns (C, C nd C 3 giving the sme vlue s shown below. Consider the determinnt of squre mtrix A [ ij ] i.e., A 3 Expnsion long first Row (R Step Multipl first element of R b ( ( + [( sum of suffixes in ] nd with the second order determinnt obtined b deleting the elements of first row (R nd first column (C of A s lies in R nd C, i.e., ( Step Multipl nd element of R b ( + [( sum of suffixes in ] nd the second order determinnt obtined b deleting elements of first row (R nd nd column (C of A s lies in R nd C, i.e., ( Step 3 Multipl third element 3 of R b ( + 3 [( sum of suffixes in 3] nd the second order determinnt obtined b deleting elements of first row (R nd third column (C 3 of A s 3 lies in R nd C 3, i.e., ( Step 4 Now the expnsion of determinnt of A, tht is, A written s sum of ll three terms obtined in steps, nd 3 bove is given b det A A ( + + ( ( or A ( ( ( 3 3

4 06 MATHEMATICS ( Note We shll ppl ll four steps together. Expnsion long second row (R Expnding long R, we get A A ( ( + ( ( ( ( 3 3 A ( Expnsion long first Column (C B expnding long C, we get A A ( + + ( ( ( ( ( 3 3

5 DETERMINANTS 07 A (3 Clerl, vlues of A in (, ( nd (3 re equl. It is left s n exercise to the reder to verif tht the vlues of A b expnding long R 3, C nd C 3 re equl to the vlue of A obtined in (, ( or (3. Hence, expnding determinnt long n row or column gives sme vlue. Remrks (i For esier clcultions, we shll expnd the determinnt long tht row or column which contins mximum number of zeros. (ii While expnding, insted of multipling b ( i + j, we cn multipl b + or ccording s (i + j is even or odd. (iii Let A 4 0 nd B 0. Then, it is es to verif tht A B. Also A nd B 0. Observe tht, A 4( B or A n B, where n is the order of squre mtrices A nd B. In generl, if A kb where A nd B re squre mtrices of order n, then A k n B, where n,, 3 Exmple 3 Evlute the determinnt Solution Note tht in the third column, two entries re zero. So expnding long third column (C 3, we get ( Exmple 4 Evlute 0 sin α cos α sin α 0 sin β cos α sin β 0.

6 08 MATHEMATICS Solution Expnding long R, we get 0 sin β sin α sin β sin α 0 0 sin α cos α sin β 0 cos α 0 cos α sin β 0 sin α (0 sin β cos α cos α (sin α sin β 0 sin α sin β cos α cos α sin α sin β 0 Exmple 5 Find vlues of x for which 3 x 3. x 4 Solution We hve 3 x 3 x 4 i.e. 3 x 3 8 i.e. x 8 Hence x ± EXERCISE 4. Evlute the determinnts in Exercises nd (i cos θ sin θ sin θ cos θ (ii x x + x x + x + 3. If A 4, then show tht A 4 A 0 4. If A 0, then show tht 3 A 7 A Evlute the determinnts (i (ii

7 DETERMINANTS 09 (iii (iv If A 3, find A Find vlues of x, if (i 4 x 4 (ii 5 6 x 3 x x 5 x 6 8. If 8 x, then x is equl to 8 6 (A 6 (B ± 6 (C 6 (D Properties of Determinnts In the previous section, we hve lernt how to expnd the determinnts. In this section, we will stud some properties of determinnts which simplifies its evlution b obtining mximum number of zeros in row or column. These properties re true for determinnts of n order. However, we shll restrict ourselves upto determinnts of order 3 onl. Propert The vlue of the determinnt remins unchnged if its rows nd columns re interchnged. Verifiction Let 3 b b b 3 c c c 3 Expnding long first row, we get b b b b b b c c3 c c3 c c (b c 3 b 3 c (b c 3 b 3 c + 3 (b c b c B interchnging the rows nd columns of, we get the determinnt b c b c b c 3 3 3

8 0 MATHEMATICS Expnding long first column, we get (b c 3 c b 3 (b c 3 b 3 c + 3 (b c b c Hence Remrk It follows from bove propert tht if A is squre mtrix, then det (A det (A, where A trnspose of A. Note If R ith row nd C ith column, then for interchnge of row nd i i columns, we will smbolicll write C i R i Let us verif the bove propert b exmple. 3 5 Exmple 6 Verif Propert for Solution Expnding the determinnt long first row, we hve ( ( ( ( B interchnging rows nd columns, we get (Expnding long first column ( ( ( ( Clerl Hence, Propert is verified. Propert If n two rows (or columns of determinnt re interchnged, then sign of determinnt chnges. Verifiction Let 3 b b b 3 c c c 3

9 DETERMINANTS Expnding long first row, we get (b c 3 b 3 c (b c 3 b 3 c + 3 (b c b c Interchnging first nd third rows, the new determinnt obtined is given b c c c 3 b b b 3 3 Expnding long third row, we get (c b 3 b c 3 (c b 3 c 3 b + 3 (b c b c [ (b c 3 b 3 c (b c 3 b 3 c + 3 (b c b c ] Clerl Similrl, we cn verif the result b interchnging n two columns. Note We cn denote the interchnge of rows b R R nd interchnge of i j columns b C i C j. Exmple 7 Verif Propert for Solution (See Exmple 6 Interchnging rows R nd R 3 i.e., R R 3, we hve Expnding the determinnt long first row, we hve ( ( ( (

10 MATHEMATICS Clerl Hence, Propert is verified. Propert 3 If n two rows (or columns of determinnt re identicl (ll corresponding elements re sme, then vlue of determinnt is zero. Proof If we interchnge the identicl rows (or columns of the determinnt, then does not chnge. However, b Propert, it follows tht hs chnged its sign Therefore or 0 Let us verif the bove propert b n exmple. Exmple 8 Evlute Solution Expnding long first row, we get Here R nd R 3 re identicl. 3 (6 6 ( (4 6 0 ( ( Propert 4 If ech element of row (or column of determinnt is multiplied b constnt k, then its vlue gets multiplied b k. Verifiction Let b c b c b c nd be the determinnt obtined b multipling the elements of the first row b k. Then k k b k c b c b c Expnding long first row, we get k (b c 3 b 3 c k b ( c 3 c 3 + k c ( b 3 b 3 k [ (b c 3 b 3 c b ( c 3 c 3 + c ( b 3 b 3 ] k

11 DETERMINANTS 3 Hence Remrks (i (ii k k b k c b c b c k b c b c b c B this propert, we cn tke out n common fctor from n one row or n one column of given determinnt. If corresponding elements of n two rows (or columns of determinnt re proportionl (in the sme rtio, then its vlue is zero. For exmple 3 b b b 3 k k k 3 0 (rows R nd R re proportionl Exmple 9 Evlute Solution Note tht (7 6(3 6( (Using Properties 3 nd 4 Propert 5 If some or ll elements of row or column of determinnt re expressed s sum of two (or more terms, then the determinnt cn be expressed s sum of two (or more determinnts. For exmple, + λ + λ + λ 3 3 b b b 3 c c c 3 λ λ λ 3 3 b b b + b b b 3 3 c c c c c c 3 3 Verifiction L.H.S. + λ + λ + λ 3 3 b b b 3 c c c 3

12 4 MATHEMATICS Expnding the determinnts long the first row, we get ( + λ (b c 3 c b 3 ( + λ (b c 3 b 3 c + ( 3 + λ 3 (b c b c (b c 3 c b 3 (b c 3 b 3 c + 3 (b c b c + λ (b c 3 c b 3 λ (b c 3 b 3 c + λ 3 (b c b c (b rerrnging terms λ λ λ 3 3 b b b3 + b b b3 R.H.S. c c c c c c 3 3 Similrl, we m verif Propert 5 for other rows or columns. b c Exmple 0 Show tht + x b + c + z 0 x z b c b c b c Solution We hve + x b + c + z b c + x z x z x z x z (b Propert (Using Propert 3 nd Propert 4 Propert 6 If, to ech element of n row or column of determinnt, the equimultiples of corresponding elements of other row (or column re dded, then vlue of determinnt remins the sme, i.e., the vlue of determinnt remin sme if we ppl the opertion R i R i + kr j or C i C i + kc j. Verifiction Let 3 b b b 3 c c c 3 nd where is obtined b the opertion R R + kr 3. + k c + k c + k c 3 3 b b b 3 c c c 3 Here, we hve multiplied the elements of the third row (R 3 b constnt k nd dded them to the corresponding elements of the first row (R. Smbolicll, we write this opertion s R R + k R 3.,

13 DETERMINANTS 5 Now, gin k c k c k c 3 3 b b b3 + b b b3 (Using Propert 5 c c c c c c 3 3 Hence Remrks (i (ii + 0 (since R nd R 3 re proportionl If is the determinnt obtined b ppling R i kr i or C i kc i to the determinnt, then k. If more thn one opertion like R i R i + kr j is done in one step, cre should be tken to see tht row tht is ffected in one opertion should not be used in nother opertion. A similr remrk pplies to column opertions. Exmple Prove tht + b + b + c 3 + b 4 + 3b + c b 0 + 6b + 3c 3 Solution Appling opertions R R R nd R 3 R 3 3R to the given determinnt, we hve Now ppling R 3 R 3 3R, we get Expnding long C, we obtin + b + b + c 0 + b b + b + b + c 0 + b b ( 0 ( 3

14 6 MATHEMATICS Exmple Without expnding, prove tht x + + z z + x z x 0 Solution Appling R R + R to, we get x + + z x + + z x + + z z x Since the elements of R nd R 3 re proportionl, 0. Exmple 3 Evlute b c bc c b Solution Appling R R R nd R 3 R 3 R, we get bc 0 b c ( b 0 c b ( c Tking fctors (b nd (c common from R nd R 3, respectivel, we get ( b ( c 0 c bc 0 b (b (c [( b + c] (Expnding long first column ( b (b c (c b + c Exmple 4 Prove tht b c + b 4bc c c + b Solution Let b + c b c + b c c + b

15 DETERMINANTS 7 Appling R R R R 3 to, we get 0 c b b c + b c c + b Expnding long R, we obtin c + b b b 0 ( c c + b c + b + ( b c + b c c c ( b + b bc b (b c c c b c + cb bc b c + bc + bc 4 bc Exmple 5 If x,, z re different nd show tht + xz 0 Solution We hve x x + x 3 0, then 3 + z z + z 3 3 x x + x z z + z 3 x x x x x 3 + (Using Propert 5 3 z z z z z x x x x ( + xz (Using C 3 C nd then C C x z z z z ( + xz z x z

16 8 MATHEMATICS ( x + xz 0 x x 0 x z x z x (Using R R R nd R 3 R 3 R Tking out common fctor ( x from R nd (z x from R 3, we get x (+ xz ( x ( z x 0 + x 0 z + x x ( + xz ( x (z x (z (on expnding long C Since 0 nd x,, z re ll different, i.e., x 0, z 0, z x 0, we get + xz 0 Exmple 6 Show tht + + b bc bc + bc + c + b b c + c Solution Tking out fctors,b,c common from R, R nd R 3, we get L.H.S. + bc + b b b + c c c Appling R R + R + R 3, we hve b c b c b c bc + b b b + c c c

17 DETERMINANTS 9 bc b c + b b b + c c c Now ppling C C C, C 3 C 3 C, we get 0 0 bc b c b 0 c b c bc ( 0 bc bc + bc + c + b R.H.S. b c Note Alterntel tr b ppling C C C nd C C C, then ppl 3 3 C C C 3. EXERCISE 4. Using the propert of determinnts nd without expnding in Exercises to 7, prove tht: x x +. b + b 0 3. z c z + c b + c q + r + z p x 5. c + r + p z + x b q + b p + q x + c r z b b c c. b c c b 0 c b b c ( + ( ( + c b c + 0 bc b c b c b

18 0 MATHEMATICS 0 b 6. 0 c 0 7. b c 0 b c b b bc 4 b c c cb c B using properties of determinnts, in Exercises 8 to 4, show tht: 8. (i b b ( b( b c( c c c (ii b c ( b( b c( c ( + b + c b c 9. x x z zx z z x (x ( z (z x (x + z + zx x + 4 x x x x + 4 x 5x x 0. (i ( ( x x x k (ii + k k ( 3 + k + k b c. (i b b c b ( + b + c 3 c c c b x + + z x (ii z + z + x ( x + + z 3 z x z + x +

19 DETERMINANTS x 3. x x ( x x x x + b b b 3. b + b ( + + b b b b c b b + bc + + b + c c cb c + Choose the correct nswer in Exercises 5 nd Let A be squre mtrix of order 3 3, then ka is equl to (A k A (B k A (C k 3 A (D 3k A 6. Which of the following is correct (A Determinnt is squre mtrix. (B Determinnt is number ssocited to mtrix. (C Determinnt is number ssocited to squre mtrix. (D None of these 4.4 Are of Tringle In erlier clsses, we hve studied tht the re of tringle whose vertices re (x,, (x, nd (x 3, 3, is given b the expression [x ( 3 + x ( 3 + x 3 ( ]. Now this expression cn be written in the form of determinnt s x x x ( Remrks (i Since re is positive quntit, we lws tke the bsolute vlue of the determinnt in (.

20 MATHEMATICS (ii (iii If re is given, use both positive nd negtive vlues of the determinnt for clcultion. The re of the tringle formed b three colliner points is zero. Exmple 7 Find the re of the tringle whose vertices re (3, 8, ( 4, nd (5,. Solution The re of tringle is given b ( ( ( + 6 ( Exmple 8 Find the eqution of the line joining A(, 3 nd B (0, 0 using determinnts nd find k if D(k, 0 is point such tht re of tringle ABD is 3sq units. Solution Let P (x, be n point on AB. Then, re of tringle ABP is zero (Wh?. So x 0 This gives ( 3 x 0 or 3x, which is the eqution of required line AB. Also, since the re of the tringle ABD is 3 sq. units, we hve This gives, k 0 3k ± 3, i.e., k. ± 3 EXERCISE 4.3. Find re of the tringle with vertices t the point given in ech of the following : (i (, 0, (6, 0, (4, 3 (ii (, 7, (,, (0, 8 (iii (, 3, (3,, (, 8

21 DETERMINANTS 3. Show tht points A (, b + c, B (b, c +, C (c, + b re colliner. 3. Find vlues of k if re of tringle is 4 sq. units nd vertices re (i (k, 0, (4, 0, (0, (ii (, 0, (0, 4, (0, k 4. (i Find eqution of line joining (, nd (3, 6 using determinnts. (ii Find eqution of line joining (3, nd (9, 3 using determinnts. 5. If re of tringle is 35 sq units with vertices (, 6, (5, 4 nd (k, 4. Then k is (A (B (C, (D, 4.5 Minors nd Cofctors In this section, we will lern to write the expnsion of determinnt in compct form using minors nd cofctors. Definition Minor of n element ij of determinnt is the determinnt obtined b deleting its ith row nd jth column in which element ij lies. Minor of n element ij is denoted b M ij. Remrk Minor of n element of determinnt of order n(n is determinnt of order n. Exmple 9 Find the minor of element 6 in the determinnt Solution Since 6 lies in the second row nd third column, its minor M 3 is given b M (obtined b deleting R nd C in. 3 Definition Cofctor of n element ij, denoted b A ij is defined b A ij ( i + j M ij, where M ij is minor of ij. Exmple 0 Find minors nd cofctors of ll the elements of the determinnt 4 3 Solution Minor of the element ij is M ij Here. So M Minor of 3 M Minor of the element 4 M Minor of the element

22 4 MATHEMATICS M Minor of the element Now, cofctor of ij is A ij. So A ( + M ( (3 3 A ( + M ( 3 (4 4 A ( + M ( 3 ( A ( + M ( 4 ( Exmple Find minors nd cofctors of the elements, in the determinnt Solution B definition of minors nd cofctors, we hve Minor of M Cofctor of A ( + M Minor of M Cofctor of A ( + M ( ( Remrk Expnding the determinnt, in Exmple, long R, we hve ( ( ( A + A + 3 A 3, where A ij is cofctor of ij sum of product of elements of R with their corresponding cofctors Similrl, cn be clculted b other five ws of expnsion tht is long R, R 3, C, C nd C 3. Hence sum of the product of elements of n row (or column with their corresponding cofctors. Note If elements of row (or column re multiplied with cofctors of n other row (or column, then their sum is zero. For exmple,

23 DETERMINANTS 5 A + A + 3 A 3 ( ( ( (since R nd R re identicl 3 3 Similrl, we cn tr for other rows nd columns. Exmple Find minors nd cofctors of the elements of the determinnt nd verif tht A 3 + A A Solution We hve M ; A ( + ( 0 0 M ; A ( + ( M ; A 3 ( +3 (30 30 M ; A ( + ( 4 4 M 5 7 M ; A ( + ( ; A 3 ( +3 (3 3 M ; A 3 ( 3+ (

24 6 MATHEMATICS M ; A 3 ( 3+ ( nd M ; A 33 ( 3+3 (8 8 Now, 3, 3 5; A 3, A 3, A 33 8 So A 3 + A A 33 ( + ( 3 ( + 5 ( EXERCISE 4.4 Write Minors nd Cofctors of the elements of following determinnts:. (i (ii b c d. (i (ii Using Cofctors of elements of second row, evlute 4. Using Cofctors of elements of third column, evlute x z zx z x. 5. If nd A ij is Cofctors of ij, then vlue of is given b (A A 3 + A A 33 (B A + A + 3 A 3 (C A + A + 3 A 3 (D A + A + 3 A Adjoint nd Inverse of Mtrix In the previous chpter, we hve studied inverse of mtrix. In this section, we shll discuss the condition for existence of inverse of mtrix. To find inverse of mtrix A, i.e., A we shll first define djoint of mtrix.

25 4.6. Adjoint of mtrix DETERMINANTS 7 Definition 3 The djoint of squre mtrix A [ ij ] n n is defined s the trnspose of the mtrix [A ij ] n n, where A ij is the cofctor of the element ij. Adjoint of the mtrix A is denoted b dj A. Let Then A A A A dj A Trnspose of A A A A A A A A A A A A A A A Exmple 3 Find dj A for A 4 Solution We hve A 4, A, A 3, A Hence dj A A A 4 3 A A Remrk For squre mtrix of order, given b A The dj A cn lso be obtined b interchnging nd nd b chnging signs of nd, i.e., We stte the following theorem without proof. Theorem If A be n given squre mtrix of order n, then A(dj A (dj A A A I, where I is the identit mtrix of order n

26 8 MATHEMATICS Verifiction 3 A A A3 Let A 3, then dj A A A A A3 A3 A 33 Since sum of product of elements of row (or column with corresponding cofctors is equl to A nd otherwise zero, we hve A (dj A A A A A A I Similrl, we cn show (dj A A A I Hence A (dj A (dj A A A I Definition 4 A squre mtrix A is sid to be singulr if A 0. For exmple, the determinnt of mtrix A 4 8 is zero Hence A is singulr mtrix. Definition 5 A squre mtrix A is sid to be non-singulr if A 0 Let A 3 4. Then A Hence A is nonsingulr mtrix We stte the following theorems without proof. Theorem If A nd B re nonsingulr mtrices of the sme order, then AB nd BA re lso nonsingulr mtrices of the sme order. Theorem 3 The determinnt of the product of mtrices is equl to product of their respective determinnts, tht is, AB A B, where A nd B re squre mtrices of the sme order Remrk We know tht (dj A A A I A A A

27 DETERMINANTS 9 Writing determinnts of mtrices on both sides, we hve ( dj A A A A A i.e. (dj A A A i.e. (dj A A A 3 ( i.e. (dj A A In generl, if A is squre mtrix of order n, then dj(a A n. (Wh? Theorem 4 A squre mtrix A is invertible if nd onl if A is nonsingulr mtrix. Proof Let A be invertible mtrix of order n nd I be the identit mtrix of order n. Then, there exists squre mtrix B of order n such tht AB BA I Now AB I. So AB I or A B (since I, AB A B This gives A 0. Hence A is nonsingulr. Conversel, let A be nonsingulr. Then A 0 Now A (dj A (dj A A A I (Theorem or A dj A dj A A I A A or AB BA I, where B A A dj Thus A is invertible nd A A A dj Exmple 4 If A , then verif tht A dj A A I. Also find A. 3 4 Solution We hve A (6 9 3 ( (3 4 0

28 30 MATHEMATICS Now A 7, A, A 3, A 3, A,A 3 0, A 3 3, A 3 0, A 33 Therefore dj A Now A (dj A ( A. I Also A A A d j Exmple 5 If A 3 nd B 4 3, then verif tht (AB B A. 3 5 Solution We hve AB Since, AB 0, (AB exists nd is given b (AB dj (AB AB 5 5 Further, A 0 nd B 0. Therefore, A nd B both exist nd re given b A 4 3 3,B

29 DETERMINANTS Therefore B A Hence (AB B A Exmple 6 Show tht the mtrix A 3 stisfies the eqution A 4A + I O, where I is identit mtrix nd O is zero mtrix. Using this eqution, find A Solution We hve A A.A 4 7 Hence A 4A + I Now A 4A + I O Therefore A A 4A I 0 0 O 0 0 or A A (A 4 A A I A (Post multipling b A becuse A 0 or A (A A 4I A or AI 4I A or A 4I A 0 4 Hence 3 A EXERCISE 4.5 Find djoint of ech of the mtrices in Exercises nd Verif A (dj A (dj A A A I in Exercises 3 nd

30 3 MATHEMATICS Find the inverse of ech of the mtrices (if it exists given in Exercises 5 to cosα sin α 0 sin α cos α. 3 7 Let A 5 nd B Verif tht (AB B A. 3. If A 3, show tht A 5A + 7I O. Hence find A For the mtrix A, find the numbers nd b such tht A + A + bi O. 5. For the mtrix A 3 3 Show tht A 3 6A + 5A + I O. Hence, find A. 6. If A Verif tht A 3 6A + 9A 4I O nd hence find A 7. Let A be nonsingulr squre mtrix of order 3 3. Then dj A is equl to (A A (B A (C A 3 (D 3 A 8. If A is n invertible mtrix of order, then det (A is equl to (A det (A (B det (A (C (D 0

31 DETERMINANTS Applictions of Determinnts nd Mtrices In this section, we shll discuss ppliction of determinnts nd mtrices for solving the sstem of liner equtions in two or three vribles nd for checking the consistenc of the sstem of liner equtions. Consistent sstem A sstem of equtions is sid to be consistent if its solution (one or more exists. Inconsistent sstem A sstem of equtions is sid to be inconsistent if its solution does not exist. Note In this chpter, we restrict ourselves to the sstem of liner equtions hving unique solutions onl Solution of sstem of liner equtions using inverse of mtrix Let us express the sstem of liner equtions s mtrix equtions nd solve them using inverse of the coefficient mtrix. Consider the sstem of equtions x + b + c z d x + b + c z d 3 x + b 3 + c 3 z d 3 b c x d Let A b c, X nd B d 3 b3 c 3 z d 3 Then, the sstem of equtions cn be written s, AX B, i.e., b c x b c 3 b3 c 3 z d d d 3 Cse I If A is nonsingulr mtrix, then its inverse exists. Now AX B or A (AX A B (premultipling b A or (A A X A B (b ssocitive propert or I X A B or X A B This mtrix eqution provides unique solution for the given sstem of equtions s inverse of mtrix is unique. This method of solving sstem of equtions is known s Mtrix Method.

32 34 MATHEMATICS Cse II If A is singulr mtrix, then A 0. In this cse, we clculte (dj A B. If (dj A B O, (O being zero mtrix, then solution does not exist nd the sstem of equtions is clled inconsistent. If (dj A B O, then sstem m be either consistent or inconsistent ccording s the sstem hve either infinitel mn solutions or no solution. Exmple 7 Solve the sstem of equtions x + 5 3x + 7 Solution The sstem of equtions cn be written in the form AX B, where A 5 x,x nd B 3 7 Now, A 0, Hence, A is nonsingulr mtrix nd so hs unique solution. Note tht A 5 3 Therefore X A B i.e. x 33 3 Hence x 3, Exmple 8 Solve the following sstem of equtions b mtrix method. 3x + 3z 8 x + z 4x 3 + z 4 Solution The sstem of equtions cn be written in the form AX B, where We see tht 3 3 x 8 A, X nd B 4 3 z 4 A 3 ( 3 + ( (

33 DETERMINANTS 35 Hence, A is nonsingulr nd so its inverse exists. Now A, A 8, A 3 0 A 5, A 6, A 3 A 3, A 3 9, A 33 7 Therefore A So X A B i.e. x z Hence x, nd z 3. Exmple 9 The sum of three numbers is 6. If we multipl third number b 3 nd dd second number to it, we get. B dding first nd third numbers, we get double of the second number. Represent it lgebricll nd find the numbers using mtrix method. Solution Let first, second nd third numbers be denoted b x, nd z, respectivel. Then, ccording to given conditions, we hve x + + z 6 + 3z x + z or x + z 0 This sstem cn be written s A X B, where x A 0 3, X nd B z Here A ( + 6 (0 3 + ( Now we find dj A 6 0 A ( + 6 7, A (0 3 3, A 3 A ( + 3, A 0, A 3 ( 3 A 3 (3, A 3 (3 0 3, A 33 ( 0

34 36 MATHEMATICS Hence dj A Thus A 7 3 A dj (A Since X A B X or x z Thus x,, z EXERCISE 4.6 Exmine the consistenc of the sstem of equtions in Exercises to 6.. x +. x 5 3. x x x + 4 x x + + z 5. 3x z 6. 5x + 4z 5 x z z x z x + + z 4 3x 5 3 5x + 6z Solve sstem of liner equtions, using mtrix method, in Exercises 7 to x x 9. 4x 3 3 7x x x x + 3. x + + z. x + z 4 3x + 5 x z 3 x + 3z 0 3 5z 9 x + + z 3. x z 5 4. x + z 7 x + z 4 3x + 4 5z 5 3x z 3 x + 3z

35 DETERMINANTS If A , find A. Using A solve the sstem of equtions x 3 + 5z 3x + 4z 5 x + z 3 6. The cost of 4 kg onion, 3 kg whet nd kg rice is Rs 60. The cost of kg onion, 4 kg whet nd 6 kg rice is Rs 90. The cost of 6 kg onion kg whet nd 3 kg rice is Rs 70. Find cost of ech item per kg b mtrix method. Miscellneous Exmples Exmple 30 If, b, c re positive nd unequl, show tht vlue of the determinnt b c b c c b is negtive. Solution Appling C C + C + C 3 to the given determinnt, we get + b + c b c + b + c c + b + c b ( + b + c b c c b b c ( + b + c 0 c b c (ApplingR R R,ndR 3 R 3 R 0 b b c ( + b + c [(c b (b c ( c ( b] (Expnding long C ( + b + c( b c + b + bc + c ( + b + c ( + b + c b bc c ( + b + c [( b + (b c + (c ] which is negtive (since + b + c > 0 nd ( b + (b c + (c > 0

36 38 MATHEMATICS Exmple 3 If, b, c, re in A.P, find vlue of b c Solution Appling R R + R 3 R to the given determinnt, we obtin b c Exmple 3 Show tht 0 (Since b + c ( + z x zx ( + x x z z ( + xz z x xz (x + + z 3 Solution Appling R xr, R R, R zr to nd dividing b xz, we get 3 3 xz ( + x z x x z ( + x x z z ( + xz z z x Tking common fctors x,, z from C C nd C 3, respectivel, we get xz xz ( + z x x ( + x z ( + z z x Appling C C C, C 3 C 3 C, we hve ( + z x ( + z x ( + z ( x + z 0 z 0 ( x + z

37 DETERMINANTS 39 Tking common fctor (x + + z from C nd C 3, we hve (x + + z Appling R R (R + R 3, we hve ( + z x ( + z x ( + z ( x + z 0 z 0 ( x + z (x + + z z z x z + 0 z x + 0 z Appling C (C + C nd C C C 3 + 3, we get z (x + + z z 0 0 x+ z z z z x+ Finll expnding long R, we hve (x + + z (z [(x + z (x + z] (x + + z (z (x + x + xz (x + + z 3 (xz 0 Exmple 33 Use product to solve the sstem of equtions x + z 3z 3x + 4z 0 Solution Consider the product

38 40 MATHEMATICS Hence Now, given sstem of equtions cn be written, in mtrix form, s follows x z x or 0 3 z Hence x 0, 5 nd z 3 Exmple 34 Prove tht + bx c + dx p + qx c p x + b cx + d px + q ( x b d q u v w u v w Solution Appling R R x R to, we get ( x c( x p( x x + b cx + d px + q u v w c p ( x x + b cx + d px + q u v w

39 DETERMINANTS 4 Appling R R x R, we get c p ( x b d q u v w Miscellneous Exercises on Chpter 4 x sin θ cos θ. Prove tht the determinnt sin θ x is independent of θ. cosθ x bc 3. Without expnding the determinnt, prove tht b b c b b 3 c c b c c 3. cosα cosβ cosα sin β sin α 3. Evlute sin β cosβ 0 sin α cosβ sin α sinβ cosα 4. If, b nd c re rel numbers, nd. b+ c c+ + b c + + b b + c 0, + b b + c c+ Show tht either + b + c 0 or b c. x + x x 5. Solve the eqution x x + x 0, 0 x x x + 6. Prove tht bc c+ c + b b c b b + bc c 4 b c nd B 3 0, find AB If A (

40 4 MATHEMATICS 8. Let A 3. Verif tht 5 (i [dj A] dj (A (ii (A A 9. Evlute x x+ x + x x + x 0. Evlute x x + x x+ Using properties of determinnts in Exercises to 5, prove tht:. α α β + γ β β γ + α γ γ α + β (β γ (γ α (α β (α + β + γ. x x + px 3 + p 3 z z + pz 3 ( + pxz (x ( z (z x, where p is n sclr b + c b + 3b b + c c + c+b 3c 3( + b + c (b + bc + c 4. + p + p + q 3 + p p + q p p + 3q 6. Solve the sstem of equtions x z 5. ( ( ( sin α cosα cos α + δ sinβ cosβ cos β + δ 0 sin γ cos γ cos γ + δ

41 DETERMINANTS x z x z Choose the correct nswer in Exercise 7 to If, b, c, re in A.P, then the determinnt x + x + 3 x + x + 3 x + 4 x + b x + 4 x + 5 x + c is (A 0 (B (C x (D x 8. If x,, z re nonzero rel numbers, then the inverse of mtrix x 0 0 A 0 0 is 0 0 z (A x z (B x 0 0 xz z (C x xz 0 0 z (D xz Let A sin θ sin sin θ θ, where 0 θ π. Then sin θ (A Det(A 0 (B Det(A (, (C Det(A (, 4 (D Det(A [, 4]

42 44 MATHEMATICS Summr Determinnt of mtrix A [ ] is given b Determinnt of mtrix A is given b A b c Determinnt of mtrix A b c 3 b3 c 3 is given b (expnding long R A b c b c c b b3 c3 3 c3 3 b b c b + c b c For n squre mtrix A, the A stisf following properties. A A, where A trnspose of A. If we interchnge n two rows (or columns, then sign of determinnt chnges. If n two rows or n two columns re identicl or proportionl, then vlue of determinnt is zero. If we multipl ech element of row or column of determinnt b constnt k, then vlue of determinnt is multiplied b k. Multipling determinnt b k mens multipl elements of onl one row (or one column b k. If A [ ],then k.a k A ij If elements of row or column in determinnt cn be expressed s sum of two or more elements, then the given determinnt cn be expressed s sum of two or more determinnts. If to ech element of row or column of determinnt the equimultiples of corresponding elements of other rows or columns re dded, then vlue of determinnt remins sme.

43 DETERMINANTS 45 Are of tringle with vertices (x,, (x, nd (x 3, 3 is given b x x x 3 3 Minor of n element ij of the determinnt of mtrix A is the determinnt obtined b deleting i th row nd j th column nd denoted b M ij. Cofctor of ij of given b A ij ( i+j M ij Vlue of determinnt of mtrix A is obtined b sum of product of elements of row (or column with corresponding cofctors. For exmple, A A + A + 3 A 3. If elements of one row (or column re multiplied with cofctors of elements of n other row (or column, then their sum is zero. For exmple, A + A + 3 A 3 0 If 3 A 3, then A A A dj A A A A A A A , where A ij is cofctor of ij A (dj A (dj A A A I, where A is squre mtrix of order n. A squre mtrix A is sid to be singulr or non-singulr ccording s A 0 or A 0. If AB BA I, where B is squre mtrix, then B is clled inverse of A. Also A B or B A nd hence (A A. A squre mtrix A hs inverse if nd onl if A is non-singulr. A ( A A dj If x + b + c z d x + b + c z d 3 x + b + c z d, then these equtions cn be written s A X B, where b c x d A,X nd B b c d 3 b3 c 3 z d 3

44 46 MATHEMATICS Unique solution of eqution AX B is given b X A B, where A 0. A sstem of eqution is consistent or inconsistent ccording s its solution exists or not. For squre mtrix A in mtrix eqution AX B (i A 0, there exists unique solution (ii A 0 nd (dj A B 0, then there exists no solution (iii A 0 nd (dj A B 0, then sstem m or m not be consistent. Historicl Note The Chinese method of representing the coefficients of the unknowns of severl liner equtions b using rods on clculting bord nturll led to the discover of simple method of elimintion. The rrngement of rods ws precisel tht of the numbers in determinnt. The Chinese, therefore, erl developed the ide of subtrcting columns nd rows s in simplifiction of determinnt Mikmi, Chin, pp 30, 93. Seki Kow, the gretest of the Jpnese Mthemticins of seventeenth centur in his work Ki Fukudi no Ho in 683 showed tht he hd the ide of determinnts nd of their expnsion. But he used this device onl in eliminting quntit from two equtions nd not directl in the solution of set of simultneous liner equtions. T. Hshi, The Fkudoi nd Determinnts in Jpnese Mthemtics, in the proc. of the Toko Mth. Soc., V. Vendermonde ws the first to recognise determinnts s independent functions. He m be clled the forml founder. Lplce (77, gve generl method of expnding determinnt in terms of its complementr minors. In 773 Lgrnge treted determinnts of the second nd third orders nd used them for purpose other thn the solution of equtions. In 80, Guss used determinnts in his theor of numbers. The next gret contributor ws Jcques - Philippe - Mrie Binet, (8 who stted the theorem relting to the product of two mtrices of m-columns nd n- rows, which for the specil cse of m n reduces to the multipliction theorem. Also on the sme d, Cuch (8 presented one on the sme subject. He used the word determinnt in its present sense. He gve the proof of multipliction theorem more stisfctor thn Binet s. The gretest contributor to the theor ws Crl Gustv Jcob Jcobi, fter this the word determinnt received its finl cceptnce.

DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ

DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ All Mthemticl truths re reltive nd conditionl. C.P. STEINMETZ 4. Introduction DETERMINANTS In the previous chpter, we hve studied bout mtrices nd lgebr of mtrices. We hve lso lernt tht system of lgebric

More information

Algebra Of Matrices & Determinants

Algebra Of Matrices & Determinants lgebr Of Mtrices & Determinnts Importnt erms Definitions & Formule 0 Mtrix - bsic introduction: mtrix hving m rows nd n columns is clled mtrix of order m n (red s m b n mtrix) nd mtrix of order lso in

More information

Chapter 3 MATRIX. In this chapter: 3.1 MATRIX NOTATION AND TERMINOLOGY

Chapter 3 MATRIX. In this chapter: 3.1 MATRIX NOTATION AND TERMINOLOGY Chpter 3 MTRIX In this chpter: Definition nd terms Specil Mtrices Mtrix Opertion: Trnspose, Equlity, Sum, Difference, Sclr Multipliction, Mtrix Multipliction, Determinnt, Inverse ppliction of Mtrix in

More information

ECON 331 Lecture Notes: Ch 4 and Ch 5

ECON 331 Lecture Notes: Ch 4 and Ch 5 Mtrix Algebr ECON 33 Lecture Notes: Ch 4 nd Ch 5. Gives us shorthnd wy of writing lrge system of equtions.. Allows us to test for the existnce of solutions to simultneous systems. 3. Allows us to solve

More information

Introduction to Determinants. Remarks. Remarks. The determinant applies in the case of square matrices

Introduction to Determinants. Remarks. Remarks. The determinant applies in the case of square matrices Introduction to Determinnts Remrks The determinnt pplies in the cse of squre mtrices squre mtrix is nonsingulr if nd only if its determinnt not zero, hence the term determinnt Nonsingulr mtrices re sometimes

More information

Matrices. Elementary Matrix Theory. Definition of a Matrix. Matrix Elements:

Matrices. Elementary Matrix Theory. Definition of a Matrix. Matrix Elements: Mtrices Elementry Mtrix Theory It is often desirble to use mtrix nottion to simplify complex mthemticl expressions. The simplifying mtrix nottion usully mkes the equtions much esier to hndle nd mnipulte.

More information

Matrices and Determinants

Matrices and Determinants Nme Chpter 8 Mtrices nd Determinnts Section 8.1 Mtrices nd Systems of Equtions Objective: In this lesson you lerned how to use mtrices, Gussin elimintion, nd Guss-Jordn elimintion to solve systems of liner

More information

a a a a a a a a a a a a a a a a a a a a a a a a In this section, we introduce a general formula for computing determinants.

a a a a a a a a a a a a a a a a a a a a a a a a In this section, we introduce a general formula for computing determinants. Section 9 The Lplce Expnsion In the lst section, we defined the determinnt of (3 3) mtrix A 12 to be 22 12 21 22 2231 22 12 21. In this section, we introduce generl formul for computing determinnts. Rewriting

More information

Chapter 2. Determinants

Chapter 2. Determinants Chpter Determinnts The Determinnt Function Recll tht the X mtrix A c b d is invertible if d-bc0. The expression d-bc occurs so frequently tht it hs nme; it is clled the determinnt of the mtrix A nd is

More information

CHAPTER 2d. MATRICES

CHAPTER 2d. MATRICES CHPTER d. MTRICES University of Bhrin Deprtment of Civil nd rch. Engineering CEG -Numericl Methods in Civil Engineering Deprtment of Civil Engineering University of Bhrin Every squre mtrix hs ssocited

More information

CHAPTER 4: DETERMINANTS

CHAPTER 4: DETERMINANTS CHAPTER 4: DETERMINANTS MARKS WEIGHTAGE 0 mrks NCERT Importnt Questions & Answers 6. If, then find the vlue of. 8 8 6 6 Given tht 8 8 6 On epnding both determinnts, we get 8 = 6 6 8 36 = 36 36 36 = 0 =

More information

MATRICES AND VECTORS SPACE

MATRICES AND VECTORS SPACE MATRICES AND VECTORS SPACE MATRICES AND MATRIX OPERATIONS SYSTEM OF LINEAR EQUATIONS DETERMINANTS VECTORS IN -SPACE AND -SPACE GENERAL VECTOR SPACES INNER PRODUCT SPACES EIGENVALUES, EIGENVECTORS LINEAR

More information

Introduction To Matrices MCV 4UI Assignment #1

Introduction To Matrices MCV 4UI Assignment #1 Introduction To Mtrices MCV UI Assignment # INTRODUCTION: A mtrix plurl: mtrices) is rectngulr rry of numbers rrnged in rows nd columns Exmples: ) b) c) [ ] d) Ech number ppering in the rry is sid to be

More information

Here we study square linear systems and properties of their coefficient matrices as they relate to the solution set of the linear system.

Here we study square linear systems and properties of their coefficient matrices as they relate to the solution set of the linear system. Section 24 Nonsingulr Liner Systems Here we study squre liner systems nd properties of their coefficient mtrices s they relte to the solution set of the liner system Let A be n n Then we know from previous

More information

REVIEW Chapter 1 The Real Number System

REVIEW Chapter 1 The Real Number System Mth 7 REVIEW Chpter The Rel Number System In clss work: Solve ll exercises. (Sections. &. Definition A set is collection of objects (elements. The Set of Nturl Numbers N N = {,,,, 5, } The Set of Whole

More information

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique? XII. LINEAR ALGEBRA: SOLVING SYSTEMS OF EQUATIONS Tody we re going to tlk bout solving systems of liner equtions. These re problems tht give couple of equtions with couple of unknowns, like: 6 2 3 7 4

More information

Operations with Matrices

Operations with Matrices Section. Equlit of Mtrices Opertions with Mtrices There re three ws to represent mtri.. A mtri cn be denoted b n uppercse letter, such s A, B, or C.. A mtri cn be denoted b representtive element enclosed

More information

The Algebra (al-jabr) of Matrices

The Algebra (al-jabr) of Matrices Section : Mtri lgebr nd Clculus Wshkewicz College of Engineering he lgebr (l-jbr) of Mtrices lgebr s brnch of mthemtics is much broder thn elementry lgebr ll of us studied in our high school dys. In sense

More information

Chapter 5 Determinants

Chapter 5 Determinants hpter 5 Determinnts 5. Introduction Every squre mtri hs ssocited with it sclr clled its determinnt. Given mtri, we use det() or to designte its determinnt. We cn lso designte the determinnt of mtri by

More information

INTRODUCTION TO LINEAR ALGEBRA

INTRODUCTION TO LINEAR ALGEBRA ME Applied Mthemtics for Mechnicl Engineers INTRODUCTION TO INEAR AGEBRA Mtrices nd Vectors Prof. Dr. Bülent E. Pltin Spring Sections & / ME Applied Mthemtics for Mechnicl Engineers INTRODUCTION TO INEAR

More information

Module 6: LINEAR TRANSFORMATIONS

Module 6: LINEAR TRANSFORMATIONS Module 6: LINEAR TRANSFORMATIONS. Trnsformtions nd mtrices Trnsformtions re generliztions of functions. A vector x in some set S n is mpped into m nother vector y T( x). A trnsformtion is liner if, for

More information

THE DISCRIMINANT & ITS APPLICATIONS

THE DISCRIMINANT & ITS APPLICATIONS THE DISCRIMINANT & ITS APPLICATIONS The discriminnt ( Δ ) is the epression tht is locted under the squre root sign in the qudrtic formul i.e. Δ b c. For emple: Given +, Δ () ( )() The discriminnt is used

More information

Determinants Chapter 3

Determinants Chapter 3 Determinnts hpter Specil se : x Mtrix Definition : the determinnt is sclr quntity defined for ny squre n x n mtrix nd denoted y or det(). x se ecll : this expression ppers in the formul for x mtrix inverse!

More information

Geometric Sequences. Geometric Sequence a sequence whose consecutive terms have a common ratio.

Geometric Sequences. Geometric Sequence a sequence whose consecutive terms have a common ratio. Geometric Sequences Geometric Sequence sequence whose consecutive terms hve common rtio. Geometric Sequence A sequence is geometric if the rtios of consecutive terms re the sme. 2 3 4... 2 3 The number

More information

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24 Mtrix lger Mtrix ddition, Sclr Multipliction nd rnsposition Mtrix lger Section.. Mtrix ddition, Sclr Multipliction nd rnsposition rectngulr rry of numers is clled mtrix ( the plurl is mtrices ) nd the

More information

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics SCHOOL OF ENGINEERING & BUIL ENVIRONMEN Mthemtics An Introduction to Mtrices Definition of Mtri Size of Mtri Rows nd Columns of Mtri Mtri Addition Sclr Multipliction of Mtri Mtri Multipliction 7 rnspose

More information

Multivariate problems and matrix algebra

Multivariate problems and matrix algebra University of Ferrr Stefno Bonnini Multivrite problems nd mtrix lgebr Multivrite problems Multivrite sttisticl nlysis dels with dt contining observtions on two or more chrcteristics (vribles) ech mesured

More information

Elements of Matrix Algebra

Elements of Matrix Algebra Elements of Mtrix Algebr Klus Neusser Kurt Schmidheiny September 30, 2015 Contents 1 Definitions 2 2 Mtrix opertions 3 3 Rnk of Mtrix 5 4 Specil Functions of Qudrtic Mtrices 6 4.1 Trce of Mtrix.........................

More information

Matrix & Vector Basic Linear Algebra & Calculus

Matrix & Vector Basic Linear Algebra & Calculus Mtrix & Vector Bsic Liner lgebr & lculus Wht is mtrix? rectngulr rry of numbers (we will concentrte on rel numbers). nxm mtrix hs n rows n m columns M x4 M M M M M M M M M M M M 4 4 4 First row Secon row

More information

Engineering Analysis ENG 3420 Fall Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00

Engineering Analysis ENG 3420 Fall Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00 Engineering Anlysis ENG 3420 Fll 2009 Dn C. Mrinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00 Lecture 13 Lst time: Problem solving in preprtion for the quiz Liner Algebr Concepts Vector Spces,

More information

Matrix Solution to Linear Equations and Markov Chains

Matrix Solution to Linear Equations and Markov Chains Trding Systems nd Methods, Fifth Edition By Perry J. Kufmn Copyright 2005, 2013 by Perry J. Kufmn APPENDIX 2 Mtrix Solution to Liner Equtions nd Mrkov Chins DIRECT SOLUTION AND CONVERGENCE METHOD Before

More information

HW3, Math 307. CSUF. Spring 2007.

HW3, Math 307. CSUF. Spring 2007. HW, Mth 7. CSUF. Spring 7. Nsser M. Abbsi Spring 7 Compiled on November 5, 8 t 8:8m public Contents Section.6, problem Section.6, problem Section.6, problem 5 Section.6, problem 7 6 5 Section.6, problem

More information

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac REVIEW OF ALGEBRA Here we review the bsic rules nd procedures of lgebr tht you need to know in order to be successful in clculus. ARITHMETIC OPERATIONS The rel numbers hve the following properties: b b

More information

Matrices. Introduction

Matrices. Introduction Mtrices Introduction Mtrices - Introduction Mtrix lgebr hs t lest two dvntges: Reduces complicted systems of equtions to simple expressions Adptble to systemtic method of mthemticl tretment nd well suited

More information

20 MATHEMATICS POLYNOMIALS

20 MATHEMATICS POLYNOMIALS 0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of

More information

Computing The Determinants By Reducing The Orders By Four

Computing The Determinants By Reducing The Orders By Four Applied Mthemtics E-Notes, 10(2010), 151-158 c ISSN 1607-2510 Avilble free t mirror sites of http://wwwmthnthuedutw/ men/ Computing The Determinnts By Reducing The Orders By Four Qefsere Gjonblj, Armend

More information

MATHEMATICS FOR MANAGEMENT BBMP1103

MATHEMATICS FOR MANAGEMENT BBMP1103 T o p i c M T R I X MTHEMTICS FOR MNGEMENT BBMP Ojectives: TOPIC : MTRIX. Define mtri. ssess the clssifictions of mtrices s well s know how to perform its opertions. Clculte the determinnt for squre mtri

More information

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique? XII. LINEAR ALGEBRA: SOLVING SYSTEMS OF EQUATIONS Tody we re going to tlk out solving systems of liner equtions. These re prolems tht give couple of equtions with couple of unknowns, like: 6= x + x 7=

More information

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008 Mth 520 Finl Exm Topic Outline Sections 1 3 (Xio/Dums/Liw) Spring 2008 The finl exm will be held on Tuesdy, My 13, 2-5pm in 117 McMilln Wht will be covered The finl exm will cover the mteril from ll of

More information

Operations with Polynomials

Operations with Polynomials 38 Chpter P Prerequisites P.4 Opertions with Polynomils Wht you should lern: How to identify the leding coefficients nd degrees of polynomils How to dd nd subtrct polynomils How to multiply polynomils

More information

Chapter 14. Matrix Representations of Linear Transformations

Chapter 14. Matrix Representations of Linear Transformations Chpter 4 Mtrix Representtions of Liner Trnsformtions When considering the Het Stte Evolution, we found tht we could describe this process using multipliction by mtrix. This ws nice becuse computers cn

More information

Theoretical foundations of Gaussian quadrature

Theoretical foundations of Gaussian quadrature Theoreticl foundtions of Gussin qudrture 1 Inner product vector spce Definition 1. A vector spce (or liner spce) is set V = {u, v, w,...} in which the following two opertions re defined: (A) Addition of

More information

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies Stte spce systems nlysis (continued) Stbility A. Definitions A system is sid to be Asymptoticlly Stble (AS) when it stisfies ut () = 0, t > 0 lim xt () 0. t A system is AS if nd only if the impulse response

More information

SUMMER KNOWHOW STUDY AND LEARNING CENTRE

SUMMER KNOWHOW STUDY AND LEARNING CENTRE SUMMER KNOWHOW STUDY AND LEARNING CENTRE Indices & Logrithms 2 Contents Indices.2 Frctionl Indices.4 Logrithms 6 Exponentil equtions. Simplifying Surds 13 Opertions on Surds..16 Scientific Nottion..18

More information

Higher Checklist (Unit 3) Higher Checklist (Unit 3) Vectors

Higher Checklist (Unit 3) Higher Checklist (Unit 3) Vectors Vectors Skill Achieved? Know tht sclr is quntity tht hs only size (no direction) Identify rel-life exmples of sclrs such s, temperture, mss, distnce, time, speed, energy nd electric chrge Know tht vector

More information

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations. Lecture 3 3 Solving liner equtions In this lecture we will discuss lgorithms for solving systems of liner equtions Multiplictive identity Let us restrict ourselves to considering squre mtrices since one

More information

Things to Memorize: A Partial List. January 27, 2017

Things to Memorize: A Partial List. January 27, 2017 Things to Memorize: A Prtil List Jnury 27, 2017 Chpter 2 Vectors - Bsic Fcts A vector hs mgnitude (lso clled size/length/norm) nd direction. It does not hve fixed position, so the sme vector cn e moved

More information

N 0 completions on partial matrices

N 0 completions on partial matrices N 0 completions on prtil mtrices C. Jordán C. Mendes Arújo Jun R. Torregros Instituto de Mtemátic Multidisciplinr / Centro de Mtemátic Universidd Politécnic de Vlenci / Universidde do Minho Cmino de Ver

More information

Numerical Linear Algebra Assignment 008

Numerical Linear Algebra Assignment 008 Numericl Liner Algebr Assignment 008 Nguyen Qun B Hong Students t Fculty of Mth nd Computer Science, Ho Chi Minh University of Science, Vietnm emil. nguyenqunbhong@gmil.com blog. http://hongnguyenqunb.wordpress.com

More information

1 Linear Least Squares

1 Linear Least Squares Lest Squres Pge 1 1 Liner Lest Squres I will try to be consistent in nottion, with n being the number of dt points, nd m < n being the number of prmeters in model function. We re interested in solving

More information

Quadratic Forms. Quadratic Forms

Quadratic Forms. Quadratic Forms Qudrtic Forms Recll the Simon & Blume excerpt from n erlier lecture which sid tht the min tsk of clculus is to pproximte nonliner functions with liner functions. It s ctully more ccurte to sy tht we pproximte

More information

2. VECTORS AND MATRICES IN 3 DIMENSIONS

2. VECTORS AND MATRICES IN 3 DIMENSIONS 2 VECTORS AND MATRICES IN 3 DIMENSIONS 21 Extending the Theory of 2-dimensionl Vectors x A point in 3-dimensionl spce cn e represented y column vector of the form y z z-xis y-xis z x y x-xis Most of the

More information

Indefinite Integral. Chapter Integration - reverse of differentiation

Indefinite Integral. Chapter Integration - reverse of differentiation Chpter Indefinite Integrl Most of the mthemticl opertions hve inverse opertions. The inverse opertion of differentition is clled integrtion. For exmple, describing process t the given moment knowing the

More information

Bridging the gap: GCSE AS Level

Bridging the gap: GCSE AS Level Bridging the gp: GCSE AS Level CONTENTS Chpter Removing rckets pge Chpter Liner equtions Chpter Simultneous equtions 8 Chpter Fctors 0 Chpter Chnge the suject of the formul Chpter 6 Solving qudrtic equtions

More information

Matrices 13: determinant properties and rules continued

Matrices 13: determinant properties and rules continued Mtrices : determinnt properties nd rules continued nthony Rossiter http://controleduction.group.shef.c.uk/indexwebbook.html http://www.shef.c.uk/cse Deprtment of utomtic Control nd Systems Engineering

More information

Section 6.1 INTRO to LAPLACE TRANSFORMS

Section 6.1 INTRO to LAPLACE TRANSFORMS Section 6. INTRO to LAPLACE TRANSFORMS Key terms: Improper Integrl; diverge, converge A A f(t)dt lim f(t)dt Piecewise Continuous Function; jump discontinuity Function of Exponentil Order Lplce Trnsform

More information

If deg(num) deg(denom), then we should use long-division of polynomials to rewrite: p(x) = s(x) + r(x) q(x), q(x)

If deg(num) deg(denom), then we should use long-division of polynomials to rewrite: p(x) = s(x) + r(x) q(x), q(x) Mth 50 The method of prtil frction decomposition (PFD is used to integrte some rtionl functions of the form p(x, where p/q is in lowest terms nd deg(num < deg(denom. q(x If deg(num deg(denom, then we should

More information

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by.

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by. NUMERICAL INTEGRATION 1 Introduction The inverse process to differentition in clculus is integrtion. Mthemticlly, integrtion is represented by f(x) dx which stnds for the integrl of the function f(x) with

More information

Prepared by: M. S. KumarSwamy, TGT(Maths) Page

Prepared by: M. S. KumarSwamy, TGT(Maths) Page Prepared b: M. S. KumarSwam, TGT(Maths) Page - 77 - CHAPTER 4: DETERMINANTS QUICK REVISION (Important Concepts & Formulae) Determinant a b If A = c d, then determinant of A is written as A = a b = det

More information

MATHEMATICS AND STATISTICS 1.2

MATHEMATICS AND STATISTICS 1.2 MATHEMATICS AND STATISTICS. Apply lgebric procedures in solving problems Eternlly ssessed 4 credits Electronic technology, such s clcultors or computers, re not permitted in the ssessment of this stndr

More information

CET MATHEMATICS 2013

CET MATHEMATICS 2013 CET MATHEMATICS VERSION CODE: C. If sin is the cute ngle between the curves + nd + 8 t (, ), then () () () Ans: () Slope of first curve m ; slope of second curve m - therefore ngle is o A sin o (). The

More information

Year 11 Matrices. A row of seats goes across an auditorium So Rows are horizontal. The columns of the Parthenon stand upright and Columns are vertical

Year 11 Matrices. A row of seats goes across an auditorium So Rows are horizontal. The columns of the Parthenon stand upright and Columns are vertical Yer 11 Mtrices Terminology: A single MATRIX (singulr) or Mny MATRICES (plurl) Chpter 3A Intro to Mtrices A mtrix is escribe s n orgnise rry of t. We escribe the ORDER of Mtrix (it's size) by noting how

More information

LINEAR ALGEBRA APPLIED

LINEAR ALGEBRA APPLIED 5.5 Applictions of Inner Product Spces 5.5 Applictions of Inner Product Spces 7 Find the cross product of two vectors in R. Find the liner or qudrtic lest squres pproimtion of function. Find the nth-order

More information

( ) ( ) Chapter 5 Diffraction condition. ρ j

( ) ( ) Chapter 5 Diffraction condition. ρ j Grdute School of Engineering Ngo Institute of Technolog Crstl Structure Anlsis Tkshi Id (Advnced Cermics Reserch Center) Updted Nov. 3 3 Chpter 5 Diffrction condition In Chp. 4 it hs been shown tht the

More information

308K. 1 Section 3.2. Zelaya Eufemia. 1. Example 1: Multiplication of Matrices: X Y Z R S R S X Y Z. By associativity we have to choices:

308K. 1 Section 3.2. Zelaya Eufemia. 1. Example 1: Multiplication of Matrices: X Y Z R S R S X Y Z. By associativity we have to choices: 8K Zely Eufemi Section 2 Exmple : Multipliction of Mtrices: X Y Z T c e d f 2 R S X Y Z 2 c e d f 2 R S 2 By ssocitivity we hve to choices: OR: X Y Z R S cr ds er fs X cy ez X dy fz 2 R S 2 Suggestion

More information

SOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 2014

SOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 2014 SOLUTIONS FOR ADMISSIONS TEST IN MATHEMATICS, COMPUTER SCIENCE AND JOINT SCHOOLS WEDNESDAY 5 NOVEMBER 014 Mrk Scheme: Ech prt of Question 1 is worth four mrks which re wrded solely for the correct nswer.

More information

5.2 Exponent Properties Involving Quotients

5.2 Exponent Properties Involving Quotients 5. Eponent Properties Involving Quotients Lerning Objectives Use the quotient of powers property. Use the power of quotient property. Simplify epressions involving quotient properties of eponents. Use

More information

Chapter 3. Vector Spaces

Chapter 3. Vector Spaces 3.4 Liner Trnsformtions 1 Chpter 3. Vector Spces 3.4 Liner Trnsformtions Note. We hve lredy studied liner trnsformtions from R n into R m. Now we look t liner trnsformtions from one generl vector spce

More information

Matrix Eigenvalues and Eigenvectors September 13, 2017

Matrix Eigenvalues and Eigenvectors September 13, 2017 Mtri Eigenvlues nd Eigenvectors September, 7 Mtri Eigenvlues nd Eigenvectors Lrry Cretto Mechnicl Engineering 5A Seminr in Engineering Anlysis September, 7 Outline Review lst lecture Definition of eigenvlues

More information

AQA Further Pure 2. Hyperbolic Functions. Section 2: The inverse hyperbolic functions

AQA Further Pure 2. Hyperbolic Functions. Section 2: The inverse hyperbolic functions Hperbolic Functions Section : The inverse hperbolic functions Notes nd Emples These notes contin subsections on The inverse hperbolic functions Integrtion using the inverse hperbolic functions Logrithmic

More information

AQA Further Pure 1. Complex Numbers. Section 1: Introduction to Complex Numbers. The number system

AQA Further Pure 1. Complex Numbers. Section 1: Introduction to Complex Numbers. The number system Complex Numbers Section 1: Introduction to Complex Numbers Notes nd Exmples These notes contin subsections on The number system Adding nd subtrcting complex numbers Multiplying complex numbers Complex

More information

Continuous Random Variables

Continuous Random Variables STAT/MATH 395 A - PROBABILITY II UW Winter Qurter 217 Néhémy Lim Continuous Rndom Vribles Nottion. The indictor function of set S is rel-vlued function defined by : { 1 if x S 1 S (x) if x S Suppose tht

More information

Recitation 3: More Applications of the Derivative

Recitation 3: More Applications of the Derivative Mth 1c TA: Pdric Brtlett Recittion 3: More Applictions of the Derivtive Week 3 Cltech 2012 1 Rndom Question Question 1 A grph consists of the following: A set V of vertices. A set E of edges where ech

More information

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes Jim Lmbers MAT 169 Fll Semester 2009-10 Lecture 4 Notes These notes correspond to Section 8.2 in the text. Series Wht is Series? An infinte series, usully referred to simply s series, is n sum of ll of

More information

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions Physics 6C Solution of inhomogeneous ordinry differentil equtions using Green s functions Peter Young November 5, 29 Homogeneous Equtions We hve studied, especilly in long HW problem, second order liner

More information

TABLE OF CONTENTS 3 CHAPTER 1

TABLE OF CONTENTS 3 CHAPTER 1 TABLE OF CONTENTS 3 CHAPTER 1 Set Lnguge & Nottion 3 CHAPTER 2 Functions 3 CHAPTER 3 Qudrtic Functions 4 CHAPTER 4 Indices & Surds 4 CHAPTER 5 Fctors of Polynomils 4 CHAPTER 6 Simultneous Equtions 4 CHAPTER

More information

Lecture 3. Limits of Functions and Continuity

Lecture 3. Limits of Functions and Continuity Lecture 3 Limits of Functions nd Continuity Audrey Terrs April 26, 21 1 Limits of Functions Notes I m skipping the lst section of Chpter 6 of Lng; the section bout open nd closed sets We cn probbly live

More information

MATH34032: Green s Functions, Integral Equations and the Calculus of Variations 1

MATH34032: Green s Functions, Integral Equations and the Calculus of Variations 1 MATH34032: Green s Functions, Integrl Equtions nd the Clculus of Vritions 1 Section 1 Function spces nd opertors Here we gives some brief detils nd definitions, prticulrly relting to opertors. For further

More information

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for.

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for. 4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX Some reliminries: Let A be rel symmetric mtrix. Let Cos θ ; (where we choose θ π for Cos θ 4 purposes of convergence of the scheme)

More information

Chapter 1: Logarithmic functions and indices

Chapter 1: Logarithmic functions and indices Chpter : Logrithmic functions nd indices. You cn simplify epressions y using rules of indices m n m n m n m n ( m ) n mn m m m m n m m n Emple Simplify these epressions: 5 r r c 4 4 d 6 5 e ( ) f ( ) 4

More information

Chapter 1. Basic Concepts

Chapter 1. Basic Concepts Socrtes Dilecticl Process: The Þrst step is the seprtion of subject into its elements. After this, by deþning nd discovering more bout its prts, one better comprehends the entire subject Socrtes (469-399)

More information

Discrete Least-squares Approximations

Discrete Least-squares Approximations Discrete Lest-squres Approximtions Given set of dt points (x, y ), (x, y ),, (x m, y m ), norml nd useful prctice in mny pplictions in sttistics, engineering nd other pplied sciences is to construct curve

More information

Green s function. Green s function. Green s function. Green s function. Green s function. Green s functions. Classical case (recall)

Green s function. Green s function. Green s function. Green s function. Green s function. Green s functions. Classical case (recall) Green s functions 3. G(t, τ) nd its derivtives G (k) t (t, τ), (k =,..., n 2) re continuous in the squre t, τ t with respect to both vribles, George Green (4 July 793 3 My 84) In 828 Green privtely published

More information

Chapters 4 & 5 Integrals & Applications

Chapters 4 & 5 Integrals & Applications Contents Chpters 4 & 5 Integrls & Applictions Motivtion to Chpters 4 & 5 2 Chpter 4 3 Ares nd Distnces 3. VIDEO - Ares Under Functions............................................ 3.2 VIDEO - Applictions

More information

Lesson 1: Quadratic Equations

Lesson 1: Quadratic Equations Lesson 1: Qudrtic Equtions Qudrtic Eqution: The qudrtic eqution in form is. In this section, we will review 4 methods of qudrtic equtions, nd when it is most to use ech method. 1. 3.. 4. Method 1: Fctoring

More information

The final exam will take place on Friday May 11th from 8am 11am in Evans room 60.

The final exam will take place on Friday May 11th from 8am 11am in Evans room 60. Mth 104: finl informtion The finl exm will tke plce on Fridy My 11th from 8m 11m in Evns room 60. The exm will cover ll prts of the course with equl weighting. It will cover Chpters 1 5, 7 15, 17 21, 23

More information

Chapter 1: Fundamentals

Chapter 1: Fundamentals Chpter 1: Fundmentls 1.1 Rel Numbers Types of Rel Numbers: Nturl Numbers: {1, 2, 3,...}; These re the counting numbers. Integers: {... 3, 2, 1, 0, 1, 2, 3,...}; These re ll the nturl numbers, their negtives,

More information

Orthogonal Polynomials and Least-Squares Approximations to Functions

Orthogonal Polynomials and Least-Squares Approximations to Functions Chpter Orthogonl Polynomils nd Lest-Squres Approximtions to Functions **4/5/3 ET. Discrete Lest-Squres Approximtions Given set of dt points (x,y ), (x,y ),..., (x m,y m ), norml nd useful prctice in mny

More information

(e) if x = y + z and a divides any two of the integers x, y, or z, then a divides the remaining integer

(e) if x = y + z and a divides any two of the integers x, y, or z, then a divides the remaining integer Divisibility In this note we introduce the notion of divisibility for two integers nd b then we discuss the division lgorithm. First we give forml definition nd note some properties of the division opertion.

More information

M344 - ADVANCED ENGINEERING MATHEMATICS

M344 - ADVANCED ENGINEERING MATHEMATICS M3 - ADVANCED ENGINEERING MATHEMATICS Lecture 18: Lplce s Eqution, Anltic nd Numericl Solution Our emple of n elliptic prtil differentil eqution is Lplce s eqution, lso clled the Diffusion Eqution. If

More information

Math& 152 Section Integration by Parts

Math& 152 Section Integration by Parts Mth& 5 Section 7. - Integrtion by Prts Integrtion by prts is rule tht trnsforms the integrl of the product of two functions into other (idelly simpler) integrls. Recll from Clculus I tht given two differentible

More information

Sturm-Liouville Eigenvalue problem: Let p(x) > 0, q(x) 0, r(x) 0 in I = (a, b). Here we assume b > a. Let X C 2 1

Sturm-Liouville Eigenvalue problem: Let p(x) > 0, q(x) 0, r(x) 0 in I = (a, b). Here we assume b > a. Let X C 2 1 Ch.4. INTEGRAL EQUATIONS AND GREEN S FUNCTIONS Ronld B Guenther nd John W Lee, Prtil Differentil Equtions of Mthemticl Physics nd Integrl Equtions. Hildebrnd, Methods of Applied Mthemtics, second edition

More information

Lecture Solution of a System of Linear Equation

Lecture Solution of a System of Linear Equation ChE Lecture Notes, Dept. of Chemicl Engineering, Univ. of TN, Knoville - D. Keffer, 5/9/98 (updted /) Lecture 8- - Solution of System of Liner Eqution 8. Why is it importnt to e le to solve system of liner

More information

Elementary Linear Algebra

Elementary Linear Algebra Elementry Liner Algebr Anton & Rorres, 1 th Edition Lecture Set 5 Chpter 4: Prt II Generl Vector Spces 163 คณ ตศาสตร ว ศวกรรม 3 สาขาว ชาว ศวกรรมคอมพ วเตอร ป การศ กษา 1/2555 163 คณตศาสตรวศวกรรม 3 สาขาวชาวศวกรรมคอมพวเตอร

More information

Vyacheslav Telnin. Search for New Numbers.

Vyacheslav Telnin. Search for New Numbers. Vycheslv Telnin Serch for New Numbers. 1 CHAPTER I 2 I.1 Introduction. In 1984, in the first issue for tht yer of the Science nd Life mgzine, I red the rticle "Non-Stndrd Anlysis" by V. Uspensky, in which

More information

Lecture 1. Functional series. Pointwise and uniform convergence.

Lecture 1. Functional series. Pointwise and uniform convergence. 1 Introduction. Lecture 1. Functionl series. Pointwise nd uniform convergence. In this course we study mongst other things Fourier series. The Fourier series for periodic function f(x) with period 2π is

More information

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT SCHOOL OF ENGINEERING & BUIL ENVIRONMEN MARICES FOR ENGINEERING Dr Clum Mcdonld Contents Introduction Definitions Wht is mtri? Rows nd columns of mtri Order of mtri Element of mtri Equlity of mtrices Opertions

More information

New Expansion and Infinite Series

New Expansion and Infinite Series Interntionl Mthemticl Forum, Vol. 9, 204, no. 22, 06-073 HIKARI Ltd, www.m-hikri.com http://dx.doi.org/0.2988/imf.204.4502 New Expnsion nd Infinite Series Diyun Zhng College of Computer Nnjing University

More information

Section 3.2: Negative Exponents

Section 3.2: Negative Exponents Section 3.2: Negtive Exponents Objective: Simplify expressions with negtive exponents using the properties of exponents. There re few specil exponent properties tht del with exponents tht re not positive.

More information

Linear Algebra 1A - solutions of ex.4

Linear Algebra 1A - solutions of ex.4 Liner Algebr A - solutions of ex.4 For ech of the following, nd the inverse mtrix (mtritz hofkhit if it exists - ( 6 6 A, B (, C 3, D, 4 4 ( E i, F (inverse over C for F. i Also, pick n invertible mtrix

More information