Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems


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1 Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur emal: 1
2 . Chapter 5 Soluton of System of Lnear Equatons Module No. 6 Soluton of Inconsstent and Ill Condtoned Systems
3 In prevous modules, we have dscussed several methods to solve a system of lnear equatons. In these modules, t s assumed that the gven system s wellposed,.e. f one (or more) coeffcent of the system s slghtly changed, then there s no maor change n the soluton. Otherwse the system of equatons s called llposed or llcondtoned. In ths module, we wll dscussed about the soluton methods of the llcondtoned system of equatons. Before gong to dscuss the llcondtoned system, we defne some basc terms from lnear algebra whch are used to descrbed the methods. 6.1 Vector and matrx norms Let x = (x 1, x 2,..., x n ) be a vector of dmenson n. The norm of the vector x s the sze or length of x, and t s denoted by x. The norm s a mappng from the set of vectors to a real number. That s, t s a real number whch satsfes the followng condtons: () x 0 and x = 0 ff x = 0 (6.1) () αx = α x for any real scalar α (6.2) () x + y x + y (trangle nequalty). (6.3) Several type of norms are defned by many authors. The most use full vector norms are defned below. () x 1 = x (6.4) =1 () x 2 = n x 2 (Eucldean norm) (6.5) =1 () x = max x (maxmum norm or unform norm). (6.6) Now, we defne dfferent type of matrx norms. Let A and B be two matrces such that A + B and AB are defned. The norm of a matrx A s denoted by A and t 1
4 Soluton of Inconsstent and Ill Condtoned Systems satsfes the followng condtons () A 0 and A = 0 ff A = 0 (6.7) () αa = α A, α s a real scalar (6.8) () A + B A + B (6.9) (v) AB A B. (6.10) From (6.10), t follows that for any postve nteger k. Lke the vector norms, some common matrx norms are A k A k, (6.11) () A 1 = max a (the column norm) (6.12) () A 2 = a 2 (the Eucldean norm) (6.13) () A = max a (the row norm). (6.14) The Eucldean norm s also known as ErhardSchmdt norm or Schur norm or the Frobenus norm. The concept of matrx norm s used to study the convergence of teratve methods to solve the system of lnear equatons. It s also used to study the stablty of a system of equatons. Example 6.1 Let A = be a matrx. Fnd the matrx norms A 1, A 2 and A Soluton. A 1 = max{ , , , } = 6 A 2 = ( 4) ( 2) = 122 and A = max{ , , } = 16. 2
5 Illcondtoned system of lnear equatons Let us consder the followng system of lnear equatons. x y = x + y = 4. (6.15) It s easy to verfy that ths system of equatons has no soluton. But, for dfferent approxmate values of 1 3 the system has dfferent nterestng results. Frst we take Then the system becomes x + 0.3y = x + y = 4. (6.16) The soluton of these equatons s x = 1.3, y = 0.1. If we approxmate 1 3 as 0.33, then the reduced system of equatons s x y = x + y = 4 (6.17) and ts soluton s x = 1, y = 1. If the approxmaton s then the system s x y = x + y = 4 (6.18) and ts soluton s x = 2, y = 10. When , then the system s x y = x + y = 4 (6.19) and ts soluton s x = 100, y = 32. Note the systems of equatons (6.15)(6.19) and ther solutons. These are very confusng stuatons. What s the best approxmaton of 1 3? 0.3 or Observe that 3
6 Soluton of Inconsstent and Ill Condtoned Systems the solutons are sgnfcantly ncreased when the coeffcent of y n frst equaton s sghtly ncreased. That s, a small change n the coeffcent of y n frst equaton of the system produces large change n the soluton. These systems are called llcondtoned or llposed system. On the other hand, f the change n the soluton s small for small changes n the coeffcents, then the system s called wellcondtoned or wellposed system. Let us consder the followng system of equatons Ax = b. (6.20) Suppose one or more elements of the matrces A and/or b be changed and let them be A and b. Also, let y be the soluton of the new system,.e. A y = b. (6.21) Assumed that the changes n the coeffcents are very small. The system of equatons (6.20) s called llcondtoned when the change n y s too large compared to the soluton vector x of (6.20). Otherwse, the system of equatons s called wellcondtoned. If a system s llcondtoned then the correspondng coeffcent matrx s called an llcondtoned matrx. [ For the ] above problem,.e. for the system of equatons (6.17) coeffcent matrx s and t s an llcondtoned matrx. 3 1 When A s small then, n general, the matrx A s llcondtoned. But, the term small has no defnte meanng. So many methods are suggested to measure the llcondtoned of a matrx. One of the smple methods s defned below. Let A be a matrx and the condton number (denoted by Cond(A)) of t s defne by Cond(A) = A A 1 (6.22) where A s any type of matrx norm. If Cond(A) s large then the matrx s called llcondtoned and correspondng system of equatons s called llcondtoned system of equatons. If Cond(A) s small then the matrx A and the correspondng system of equatons are called wellcondtoned. 4
7 Let us consder the followng [ two ] matrces to[ llustrated ] the llcondtoned and wellcondtoned cases. Let A = and B = be two matrces [ ] [ ] Then A 1 = and B 1 = The Eucldean norms of A and B are A 2 = = and A 1 2 = Thus, Cond(A) = A 2 A 1 2 = = , a very large number. Hence A s llcondtoned. For the matrx B, B 2 = = and B 1 2 = Then Cond(B) = , a relatvely small quantty. Thus, the matrx B s wellcondtoned. The value of Cond(A) les between 0 and. If t s large then we say that the matrx s llcondtoned. But, there s no defnte meanng of large number. So, ths measure s not good. Now, we defne another parameter whose value les between 0 and 1. ( 1/2, Let A = [a ] be a matrx and r = a) 2 = 1, 2,..., n. The quantty ν(a) = A r 1 r 2 r n (6.23) measures the smallness of the determnant A. It can be shown that 1 ν 1. If ν(a) s closed to zero, then the matrx A s llcondtoned and f t s closed to 1, then A s wellcondtoned. [ ] 1 4 For the matrx A =, r 1 = 17, r 2 = , A = 0.12, [ ] ν(a) = = and for the matrx B =, r 1 = 34, r 2 = 8, B = 16, ν(b) = = Thus the matrx A s llcondtoned whle the matrx B s wellcondtoned as ts value s very closed to 1. 5
8 Soluton of Inconsstent and Ill Condtoned Systems 6.3 Least squares method for nconsstent system Let us consder a system of equatons whose number of equatons s not equal to number of varables. Let such system be Ax = b (6.24) where A, x and b are of order m n, n 1 and m 1 respectvely. Note that the coeffcent matrx s rectangular. Thus, ether the system has no soluton or t has nfnte number of solutons. Assumed that the system s nconsstent. So, t does not have any soluton. But, the system may have a least squares soluton. A soluton x s sad to be least squares f Ax b 0, but Ax b s mnmum. The soluton x m s called the mnmum norm least squares soluton f x m x l (6.25) for any x l such that Ax l b Ax b for all x. (6.26) Snce A s rectangular matrx, so ts soluton can be determned by the followng equaton x = A + b, (6.27) where A + s the gnverse of A. Snce the MoorePenrose nverse A + s unque, therefore the mnmum norm least squares soluton s unque. The soluton can also be determned by wthout fndng the gnverse of A. Ths method s descrbed below. If x s the exact soluton of the system of equatons Ax = b, then Ax b = 0, otherwse Ax b s a nonnull matrx of order m 1. In explct form ths vector s a 11 x 1 + a 12 x 2 + a 1n x n b 1 a 21 x 1 + a 22 x 2 + a 2n x n b 2. a m1 x 1 + a m2 x 2 + a mn x n b m 6
9 Let square of the norm Ax b be denoted by S. Therefore, S = (a 11 x 1 + a 12 x 2 + a 1n x n b 1 ) 2 +(a 21 x 1 + a 22 x 2 + a 2n x n b 2 ) 2 + +(a m1 x 1 + a m2 x 2 + a mn x n b n ) 2 m = (a x b ) 2. (6.28) =1 The quantty S s called the sum of square of resduals. Now, our am s to fnd the vector x = (x 1, x 2,..., x n ) t such that S s mnmum. The suffcent condtons for whch S to be mnmum are S x 1 = 0, S x 2 = 0,, S x n = 0 (6.29) Note that the system of equatons (6.29) s nonhomogeneous and contans n equatons wth n unknowns x 1, x 2,..., x n. Ths system of equatons can be solved by any method descrbed n prevous modules. Let x 1 = x 1, x 2 = x 2,..., x n = x n be the soluton of the equatons (6.29). Therefore, the least squares soluton of the system of equatons (6.24) s x = (x 1, x 2,..., x n) t. (6.30) The sum of square of resduals (.e. the sum of the squares of the absolute errors) s gven by S = m =1 (a x b ) 2. (6.31) Let us consder two examples to llustrate the least squares method whch s used to solve nconsstent system of equatons. [ ] 4 8 Example 6.2 Fnd gnverse of the sngular matrx A = and hence fnd a least 1 2 squares soluton of the nconsstent system of equatons 4x + 8y = 2, x + 2y = 1. [ ] [ ] [ ] Soluton. Let α 1 =, α 2 =, A 1 = [ ] [ ] 1 A + 1 = (αt 1 α 1) 1 α1 t = 4 [ ] [ ] = 4 1,
10 Soluton of Inconsstent and Ill Condtoned Systems [ ] [ ] δ 2 = A + 1 α 8 2 = 4 1 = 2, [ ] [ ] [ ] γ 2 = α 2 A 1 δ 2 = 2 = = 0 (a null vector), [ ] [ ] β 2 = (1 + δ2 tδ 2) 1 δ2 ta+ 1 = = [ ] δ 2 β 2 = Therefore, 4 85 Ths s the gnverse of A. A + 2 = [ A + 1 δ 2 β 2 β 2 ] = [ Second Part: In matrx notaton, the gven system of equatons s Ax = b, where [ ] [ ] [ ] 4 8 x 2 A =, x =, b =. 1 2 y 1 Note that the coeffcent matrx s sngular. So, t has no conventonal soluton. But, the least squares soluton of ths system of equaton s x = A + b,.e. Hence, the least squares soluton s Example [ ] [ ] [ ] x = /85 = /85 x = 9 85, y = Fnd the least squares soluton of the followng system of lnear equatons x + 2y = 2.0, x y = 1.0, x + 3y = 2.3, and 2x + y = 2.9. Also, estmate the resdual. Soluton. Let x, y be the least squares soluton of the gven system of equatons. Then the sum of square of resduals S s S = (x + 2y 2.0) 2 + (x y 1.0) 2 + (x + 3y 2.3) 2 + (2x + y 2.9) 2. Now, the problem s to fnd the values of x and y n such a way that S s mnmum. Thus, 8 S S = 0 and x y = 0. ].
11 Therefore the normal equatons are, 2(x + 2y 2.0) + 2(x y 1.0) + 2(x + 3y 2.3) + 4(2x + y 2.9) = 0 and 4(x + 2y 2.0) 2(x y 1.0) + 6(x + 3y 2.3) + 2(2x + y 2.9) = 0. After smplfcaton, these equatons reduce to 7x +6y = 11.1 and 6x +15y = The soluton of these equatons s x = 1.3 and y = 1 = Ths s the least 3 squares soluton of the gven system of equatons. The sum of the square of resduals s S = ( ) 2 + ( ) 2 + ( ) 2 + ( ) 2 = Method to solve llcondtoned system It s very dffcult to solve a system of llcondtoned equatons. Few methods are avalable to solve an llcondtoned system of lnear equatons. One smple concept to solve an llcondtoned system s to carry out the calculatons wth large number of sgnfcant dgts. But, computaton wth more sgnfcant dgts takes much tme. One better method s to mprove upon the accuracy of the approxmate soluton by an teratve method. Such an teratve method s consder below. Let us consder the followng llcondtoned system of equatons a x = b, = 1, 2,..., n. (6.32) Let { x 1, x 2,..., x n } be an approxmate soluton of (6.32). Snce ths s an approxmate soluton, therefore a x s not necessarly equal to b. For ths soluton, let the rght hand vector be b,.e. b = b. Thus, for ths soluton the equaton (6.32) becomes a x = b, = 1, 2,..., n. (6.33) 9
12 Soluton of Inconsstent and Ill Condtoned Systems Subtractng (6.33) from (6.32), we get.e., a (x x ) = (b b ) a ε = d (6.34) where ε = x x, d = b b, = 1, 2,..., n. Now, equaton (6.34) s agan a system of lnear equatons whose unknowns are ε 1, ε 2,..., ε n. By solvng these equatons we obtaned the values of ε s. Hence, the new soluton s gven by x = ε + x and ths soluton s better approxmaton to x s. Ths technque may be repeated to get more better soluton. 6.5 The relaxaton method The relaxaton method, nvented by Southwell n 1946, s an teratve method used to solved a system of lnear equatons. Let a x = b, (6.35) be the th, = 1, 2,..., n equaton of a system of lnear equatons. Let x (k) = (x (k) 1, x(k) 2,..., x(k) n ) t be the kth terated soluton of the system of lnear equatons. Then a x (k) b, = 1, 2,..., n. Now, we denote the kth terated resdual for the th equaton by r (k). Therefore, the value of r (k) s gven by r (k) = b a x (k), = 1, 2,..., n. (6.36) If r (k) = 0 for all = 1, 2,..., n, then (x (k) 1, x(k) 2,..., x(k) n ) t s the exact soluton of the gven system of equatons. If the resduals are not zero or not small for all equatons, then apply the same method to reduce the resduals. 10
13 In relaxaton method, the soluton can be mproved successvely by reducng the largest resdual to zero at that teraton. To get the fast convergence, the equatons are rearranged n such a way that the largest coeffcents n the equatons appear on the dagonals,.e. the coeffcent matrx becomes dagonally domnant. The am of ths method s to reduce the largest resdual to zero. Let r p be the largest resdual (n magntude) occurs at the pth equaton for a partcular teraton. Then the value of the varable x p be ncreased by dx p where dx p = r p a pp. That s, x p s replaced by x p + dx p to relax r p,.e. to reduce r p to zero. Then the modfed soluton after ths teraton s ( ) x (k+1) = x (k) 1, x(k) 2,..., x(k) p 1, x p + dx p, x (k) p+1,..., x(k) n. The method s repeated untl all the resduals become zero or tends to zero. Example 6.4 Solve the followng system of lnear equatons by relaxaton method takng (0, 0, 0) as ntal soluton 27x + 6y z = 54, 6x + 15y + 2z = 72, x + y + 54z = 110. Soluton. The gven system of equatons s dagonally domnant. The resduals r 1, r 2, r 3 are gven by the followng equatons r 1 = 54 27x 6y + z r 2 = 72 6x 15y 2z r 3 = 110 x y 54z. Here, the ntal soluton s (0, 0, 0),.e. x = y = z = 0. Therefore, the resduals are r 1 = 54, r 2 = 72, r 3 = 110. The largest resdual n magntude s r 3. Thus, the thrd equaton has more error and we have to mprove x 3. Then the ncrement dx 3 n x 3 s now calculated as dx 3 = r 3 = 110 a = Thus the frst terated soluton s (0, 0, ),.e. (0, 0, 2.037). In next teraton we determne the new resduals of large magntudes and relax t to zero. The process s repeated untl all the resduals become zero or very small. 11
14 Soluton of Inconsstent and Ill Condtoned Systems All steps of all teratons are shown below: resduals max ncrement soluton k r 1 r 2 r 3 (r 1, r 2, r 3 ) p dx p x y z In ths case, all resduals are very small. The soluton of the gven system of equatons s x 1 = 1.166, x 2 = 4.075, x 3 = 1.940, correct upto three decmal places. 6.6 Successve overrelaxaton (S.O.R.) method The relaxaton method can be modfed to acheve fast convergence. For ths purpose, a sutable relaxaton factor w s ntroduced. The th equaton of the system of equatons a x = b, = 1, 2,..., n (6.37) s a x = b. 12 Ths equaton can be wrtten as
15 a x + a x = b. (6.38) = Let ( x (0) 1, x(0) 2,..., ) x(0) n be the ntal soluton and ( x (k+1) 1, x (k+1) 2,..., x (k+1) 1, x (k), x (k) +1,..., ) x(k) n, be the soluton when th equaton beng consder. Then the equaton (6.38) becomes 1 a x (k+1) + = a x (k) = b. (6.39) Snce ( x (k+1) 1, x (k+1) 2,..., x (k+1) 1, x (k), x (k) +1,..., ) x(k) n s an approxmate soluton of the gven system of equatons, therefore the resdual at the th equaton s determne from the followng equaton: 1 r = b a x (k+1) = a x (k), = 1, 2,..., n. (6.40) We denote the dfferences of x s at two consecutve teratons by ε (k) as ε (k) = x (k+1) x (k). In the successve overrelaxaton (SOR) method, t s assumed that where w s a scalar, called the relaxaton factor. Thus, the equaton (6.41) becomes a x (k+1) and t s defned a ε (k) = w r, = 1, 2,..., n, (6.41) = a x (k) [ 1 w a x (k+1) + = = 1, 2,..., n; k = 0, 1, 2,... The teraton process s repeated untl desred accuracy s acheved. a x (k) b ], (6.42) The above teraton method s called the overrelaxaton method when 1 < w < 2, and s called the under relaxaton method when 0 < w < 1. When w = 1, the method becomes well known GaussSedal s teraton method. 13
16 Soluton of Inconsstent and Ill Condtoned Systems The proper choce of w can speed up the convergence of the teraton scheme and t depends on the gven system of equatons. Example 6.5 Solve the followng system of lnear equatons 4x 1 + 2x 2 + x 3 = 5, x 1 + 5x 2 + 2x 3 = 6, x 1 + x 2 + 7x 3 = 2 by SOR method taken relaxaton factor w = Soluton. The SOR teraton scheme for the gven system of equatons s [ ] 4x (k+1) 1 = 4x (k) x (k) 1 + 2x (k) 2 + x (k) 3 5 [ ] 5x (k+1) 2 = 5x (k) x (k+1) 1 + 5x (k) 2 + 2x (k) 3 6 [ ] 7x (k+1) 3 = 7x (k) x (k+1) 1 + x (k+1) 2 + 7x (k) 3 2. Let x (0) 1 = x (0) 2 = x (0) 3 = 0. The calculatons of all teratons are shown below: k x 1 x 2 x The solutons at teratons 8th and 9th are same. Hence, the requred soluton s x 1 = , x 2 = , x 3 = correct up to four decmal places. 14
n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0
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