CHAPTER 4. Introduction to Solving. Linear Algebraic Equations

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1 A SERIES OF CLASS NOTES TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS LINEAR CLASS NOTES: A COLLECTION OF HANDOUTS FOR REVIEW AND PREVIEW OF LINEAR THEORY INCLUDING FUNDAMENTALS OF LINEAR ALGEBRA CHAPTER 4 Introduction to Solving Linear Algeraic Equations 1. Systems, Solutions, and Elementary Equation Operations 2. Introduction to Gauss Elimination 3. Connection with Matrix Algera and Astract Linear Algera 4. Possiilities for Linear Algeraic Equations 5. Writing Solutions To Linear Algeraic Equations 6. Elementary Matrices and Solving the Vector Equation Ch. 4 Pg. 1

2 Handout #1 SYSTEMS, SOLUTIONS, AND Prof. Moseley ELEMENTARY EQUATION OPERATIONS In high school you learned how to solve two linear (and possily nonlinear) equations in two unknowns y the elementary algeraic techniques of addition and sustitution to eliminate one of the variales. These techniques may have een extended to three and four variales. Also, graphical or geometric techniques may have een developed using the intersection of two lines or three planes. Later, you may have een introduced to a more formal approach to solving a system of m equations in n unknown variales. a 11 x 1 + a 12 x a 1n x n = 1 a 21 x 1 + a 22 x a 2n x n = 2 (1) a m1 x 1 + a m2 x a mn x n = m We assume all of the scalars a ij, x i, and j are elements in a field K. (Recall that a field is an astract algera structure that may e defined informally as a numer system where you can add, sutract, multiply, and divide. Examples of a field are Q, R, and C. However, N and Z are not fields. Unless otherwise noted, the scalars can e assumed to e real or complex numers. i.e., K = R or K = C.) The formal process for solving m linear algeraic equations in n unknowns is called Gauss Elimination. We need a formal process to prove that a solution (or a parametric expression for an infinite numer of solutions) can always e otained in a finite numer of steps (or a proof that no solution exists), thus avoiding the pitfall of a "circular loop" which may result from ad hoc approaches taken to avoid fractions. Computer programs using variations of this algorithm avoid laorious arithmetic and handle prolems where the numer of variales is large. Different programs may take advantage of particular characteristics of a category of linear algeraic equations (e.g., anded equations). Software is also availale for iterative techniques which are not discussed here. Another technique which is theoretically interesting, ut only useful computationally for very small systems is Cramer Rule which is discussed in Chapter 7. DEFINITION #1. A solution to (1) is an n-tuple (finite sequence) x 1, x 2,..., x n in K n (e.g., R n or C n ) such that all of the equations in (1) are true. It can e considered to e a row vector [ x 1, x 2,..., x n ] or as a column vector [ x 1, x 2,..., x n ] T using the transpose notation. When we later formulate the prolem given y the scalar equatiuons (1) as a matrix or vector equation, we will need our unknown vector to e a column vector, hence we use column vectors. If we use column vectors, the solution set for (1) is the set S = { x = [ x 1, x 2,..., x n ] K n : when x 1, x 2,..., x n are sustituted into (1), all of the equations in (1) are satisfied}. Ch. 4 Pg. 2

3 DEFINITION #2. Two systems of linear algeraic equations are equivalent if their solution sets are equal (i.e., have the same elements). As with a single algeraic equation, there are algeraic operations that can e performed on a system that yield a new equivalent system. We also refer to these as equivalent equation operations (EEO s). However, we will restrict the EEO s we use to three elementary equation operations. DEFINITION #3. The Elementary Equation Operations (EEO s) are 1. Exchange two equations 2. Multiply an equation y a nonzero constant. 3. Replace an equation y itself plus a scalar multiple of another equation. The hope of elimination using EEO s is to otain, if possile, an equivalent system of n equations that is one-way coupled with new coefficients a ij as follows: a 11 x 1 + a 12 x a 1n x n = 1 a 22 x a 2n x n = 2 (2) a nn x n = n where a ii 0 for i = 1, 2,...,n. Since these equations are only one-way coupled, the last equation may then e solved for x n and sustituted ack into the previous equation to find x n -1. This process may e continued to find all of the x i s that make up the unique (vector) solution. Note that this requires that m n so that there are at least as many equations as unknowns (some equations may e redundant) and that all of the diagonal coefficients a ii are not zero. This is a very important special case. The nonzero coefficients a ii are called pivots. Although other operations on the system of equations can e derived, the three EEO s in Definition #3 are sufficient to find a one-way coupled equivalent system, if one exists. If sufficient care is not taken when using other operations such as replacing an equation y a linear comination of the equations, a new system which is not equivalent to the old one may result. Also, if we restrict ourselves to these three EEO s, it is easier to develop computational algorithms that can e easily programed on a computer. Note that applying one of these EEO s to a system of equations (the Original System or OS) results in a new system of equations (New System or NS). Our claim is that the systems OS and NS have the same solution set. THEOREM #1. The new system (NS) of equations otained y applying an elementary equation operation to a system (OS) of equations is equivalent to the original system (OS) of equations. Proof idea. By Definition#2 we need to show that the two systems have the same solution set. Ch. 4 Pg. 3

4 EEO # 1 Whether a given x =[x 1, x 2,..., x n ] is a solution of a given scalar equation is clearly not dependent on the order in which the equations are written down. Hence whether a given x =[x 1, x 2,..., x n ] is a solution of all equations in a system of equations is not dependent on the order in which the equations are written down EEO # 2 If x =[x 1, x 2,..., x n ] satisfies. a i1 x 1 + ka i2 x a in x n = i (3) then y the theorems of high school algera, for any scalar k it also satisfies ka i1 x 1 + ka i2 x ka in x n = k i. (4) The converse can e shown if k0 since k will have a multiplicative inverse. EEO # 3. If x = [x 1,..., x n ] T satisfies each of the original equations, then it satisfies a i1 x a in x n + k(a j1 x a jn x n ) = i + k j. (5) Conversely, if (5) is satisfied and a j1 x a jn x n = j (6) is satisfied then (5) - k (6) is also satisfied. But this is just (3). QED THEOREM #2. Every EEO has in inverse EEO of the same type. Proof idea. EEO # 1. The inverse operation is to switch the equations ack. EEO # 2. The inverse operation is to divide the equation y k(0); that is, to multiply the equation y 1/k. EEO # 3. The inverse operation is to replace the equation y itself minus k (or plus k) times the previously added equation (instead of plus k times the equation). QED EXERCISES on Systems, Solutions, and Elementary Equation Operations EXERCISE #1. True or False. 1. The formal process for solving m linear algeraic equations in n unknowns is called Gauss Elimination 2. Another technique for solving n linear algeraic equations in n unknowns is Cramer Rule 3. A solution to a system of m linear algeraic equations in n unknowns is an n-tuple Ch. 4 Pg. 4

5 (finite sequence) x 1, x 2,..., x n in K n (e.g., R n or C n ) such that all of the equations are true. Ch. 4 Pg. 5

6 Handout #2 INTRODUCTION TO GAUSS ELIMINATION Professor Moseley Gauss elimination can e used to solve a system of m linear algeraic equations over a field K in n unknown variales x 1, x 2,..., x n in a finite numer of steps. Recall a 11 x 1 + a 12 x a 1n x n = 1 a 21 x 1 + a 22 x a 2n x n = 2 (1) a m1 x 1 + a m2 x a mn x n = m DEFINITION #1. A solution of to (1) is an n-tuple x 1, x 2,..., x n which may e considered as a (row) vector [ x 1, x 2,..., x n ] (or column vector) in K n (usually K is R or C ) such that all of the equations in (1) are true. The solution set for (1) is the set S = { [ x 1, x 2,..., x n ] K n : all of the equations in (1) are satisfied}. That is, the solution set is the set of all solutions. The set is the set where we look for solutions. In this case it is K n. Two systems of linear algeraic equations are equivalent if their solution set are the same (i.e., have the same elements). Recall also the three elementary equation operations (EEO s) that can e used on a set of linear equations which do not change the solution set. 1. Exchange two equations 2. Multiply an equation y a nonzero constant. 3. Replace an equations y itself plus a scalar multiple of another equation. Although other operations on the system of equations can e derived, if we restrict ourselves to these operations, it is easier to develop computational algorithms that can e easily programed on a computer. DEFINITION #2. The coefficient matrix is the array of coefficients for the system (not including the right hand side, RHS). a a a a a a a a a n n m1 m21 mn (2) Ch. 4 Pg. 6

7 DEFINITION #3. The augmented (coefficient) matrix is the coefficient matrix augmented y the values from the right hand side (RHS). x 1 x 2 x n +, *a 11 a 12 a 1n * 1 * Represents the first equation *a 21 a 22 a 2n * 2 * Represents the second equation * * * (3) * * * * * * *a m1 a m2 a mn * m * Represents the m th equation. - Given the coefficient matrix A and the right hand side ( vector ), we denote the associated augmented matrix as [A ]. (Note the slight ause of notation since A actually includes the rackets. This should not cause confusion.) ELEMENTARY ROW OPERATIONS. All of the information contained in the equations of a linear algeraic system is contained in the augmented matrix. Rather than operate on the original equations using the elementary equation operations (EEO s) listed aove, we operate on the augmented matrix using Elementary Row Operations (ERO's). DEFINITION #4. We define the Elementary Row Operations (ERO's) 1. Exchange two rows (avoid if possile since the determinant of the new coefficient matrix changes sign). 2. Multiply a row y a non zero constant (avoid if possile since the determinant of the new coefficient matrix is a multiple of the old one). 3. Replace a row y itself plus a scalar multiple of another row (the determinant of the new coefficient matrix is the same as the old one). There is a clear one-to-one correspondence etween systems of linear algeraic equations and augmented matrices including a correspondence etween EEO s and ERO s. We say that the two structures are isomorphic. Using this correspondence and the theorems on EEO s from the previous handout, we immediately have the following theorems. THEOREM #1. Suppose a system of linear equations is represented y an augmented matrix which we call the original augmented matrix (OAM) and that the OAM is operated on y an ERO to otain a new augmented matrix (NAM). Then the system of equations represented y the new augmented matrix (NAM) has the same solution set as the system represented y the original augmented matrix (OAM). THEOREM #2. Every ERO has an inverse ERO of the same type. DEFINITION #5. If A and B are m n (coefficient or augmented) matrices over the field F, we say that B is row-equivalent to A if B can e otained from A y finite sequence of ERO's. Ch. 4 Pg. 7

8 THEOREM #3. Row-equivalence is an equivalence relation (Recall the definition of equivalence relation given in the remedial notes or look up it up in a Modern Algera text). THEOREM #4 If A and B are m n augmented matrices which are row-equivalent, then the systems they represent are equivalent (i.e., have the same solution set). Proof idea. Suppose A = A 0 A 1 A k = B. Using induction and Theorem #1, the two systems can e shown to have the same solution set. DEFINITION #6. If A is a m n (coefficient or augmented) matrices over the field F, we call the first nonzero entry in each row its leading entry. We say that A is in row-echelon form if: 1. All entries elow a leading entry are zero, 2. For i =2,..., m, the leading entry in row i is to the right of the leading entry in row (i1), 3. All rows containing only zeros are elow any rows containing nonzero entries. If, in addition, 4. The leading entry in each row is one, 5. All entries aove a leading entry are zero, then A is in reduced-row-echelon form (or row-reduced-echelon form). If A is in row-echelon form (or reduced-row-echelon form) we refer to the leading entries as pivots. For any matrix A, Gauss Elimination (GE) will always otain a row-equivalent matrix U that is in row-echelon form. Gauss-Jordan Elimination (GJE) will yield a row-equivalent matrix R that is in GE GJE reduced-row-echelon form. A U c R d EXAMPLE #1. To illustrate Gauss elimination we consider an example: 2x + y + z = 1 (1) 4x + y = -2 (2) 2x + 2y + z = 7 (3) It does not illustrate the procedure completely, ut is a good starting point. The solution set S for (1), (2) and (3) is the set of all ordered triples, [x,y,z] which satisfy all three equations. That is, S = { x = [x, y, z] R 3 : Equations (1), (2), and (3) are true } The coefficient and augmented matrices are Ch. 4 Pg. 8

9 x y z A = [A ] = x y z RHS represents equation represents equation represents equation 3 Note that matrix multiplication is not necessary for the process of solving a system of linear algeraic equations. However, you may e aware that we can use matrix multiplication to write x the system (1), (2), and (3) as A = where now it is mandatory that e a column vector instead of a row vector. Also to save space and paper.) = [1, 2, 7] T is a column vector. (We use the transpose notation GAUSS ELIMINATION. We now use the elementary row operations (ERO's) on the example in a systematic way known as (naive) Gauss (Jordan) Elimination to solve the system. The process can e divided into three (3) steps. Step 1. Forward Elimination (otain zeros elow the pivots).this step is also called the (forward) sweep phase R 2 2R R 3 R R 3 3R This completes the forward elimination step. The pivots are the diagonal elements 2, -1, and -4. Note that in getting zeros elow the pivot 2, we can do 2 ERO s and only rewrite the matrix once. The last augmented matrix represents the system. 2x + y + z = 1 We could now use 4z = 4 z = 1 y 2z = 4 ack sustitution y = 4 + 2z = 4 + 2(1) = 2 y = 2 4z = 4. to otain 2x = 1 y z = 1 (2) (1) = 2 x = 1. The unique solution is sometimes written in the scalar form as x = 1, y = 2, and z = 1, ut is more correctly written in the vector form as the column vector [1,2,1] T. Instead of ack sustituting with the equations, we can use the following two additional steps using the augmented matrix. When these steps are used, we refer to the process as Gauss-Jordan elimination. Step 2. Gauss-Jordan Normalization (Otain ones along the diagonal). x Ch. 4 Pg. 9

10 1/ 2 R R 2 1 1/ 4 R / 2 1/ 2 1/ We could now use ack sustitution ut instead we can proceed with the augmented matrix. Step 3. Gauss-Jordan Completion Phase (Otain zeros aove the pivots) Variation #1 Right to Left. nr1 1/2nR3 1 1/2 1/2 1/2 nr1 1/2nR 2 1 1/ x 1 R 2 2R y z 1 Variation #2 (Left to Right) nr1 1/ 2 R 2 1 1/2 1/2 1/2 nr1 1/2nR /2 3/ x R 2 2R y z 1 x = [-1,2,1] Note that oth variations as well as ack sustitution result in the same solution,. It should also e reasonaly clear that this algorithm can e programmed on a computer. It should also e clear that the aove procedure will give a unique solution for n equations in n unknowns in a finite numer of steps provided zeros never appear on the diagonal. The case when zeros appear on the diagonal and we still have a unique solution is illustrated elow. The more general case of m equations in n unknowns where three possiilities exist is discussed later. EXAMPLE #2. To illustrate the case of zeros on the diagonal that are eliminated y row exchanges consider: y 2 z = 4 (4) 4 z = 4 (5) 2x + y + z = 1 (6) The augmented matrix is x y z RHS [A ] = representsnequationn representsnequationn representsnequationn3 Note that there is a zero in the first row first column so that Gauss Elimination temporarily reaks down. However, note that the augmented matrix is row equivalent to those in the previous T Ch. 4 Pg. 10

11 prolem since it is just the matrix at the end of the forward step of that prolem with the rows in a different order To estalish a standard convention for fixing the reakdown, we go down the first column until we find a nonzero numer. We then switch the first row with the first row with a nonzero entry in the first column. (If all of the entries in the first column are zero, then x can e anything and is not really involved in the prolem.) Switching Rows one and three we otain The 2 in the first row, first column is now our first pivot. We go to the second row, second column. Unfortunately, it is also a zero. But the third row, second column is not so we switch rows We now have the same augmented matrix as given at the end of the forward step for the previous prolem. Hence the solution is x = 1, y = 2, and z = 1. This can e written as the column vector x = [1, 2, 1 ] T. EXERCISES on Introduction to Gauss Elimination EXERCISE #1. True or False. 1. An elementary equation operation (EEO) that can e used on a set of linear algeraic equations which does not change the solution set is Exchange two equations. 2. An elementary equation operation (EEO) that can e used on a set of linear algeraic equations which does not change the solution set is Multiply an equation y a nonzero constant. 3. An elementary equation operation (EEO) that can e used on a set of linear algeraic equations which does not change the solution set is Replace an equations y itself plus a scalar multiple of another equation. 4. An elementary row operation (ERO) of type one that can e used on a matrix is Exchange two rows. 5. An elementary row operation (ERO) of type two that can e used on a matrix is Multiply a row y a nonzero constant 6. An elementary row operation (ERO) of type three that can e used on a matrix is Replace a row y itself plus a scalar multiple of another row. 7. There is a clear one-to-one correspondence etween systems of linear algeraic equations and augmented matrices including a correspondence etween EEO s and ERO s so that we say that the two structures are isomorphic. 8. Every ERO has an inverse ERO of the same type. Ch. 4 Pg. 11

12 9. If A and B are m n (coefficient or augmented) matrices over the field K, we say that B is row-equivalent to A if B can e otained from A y finite sequence of ERO's. 10. Row-equivalence is an equivalence relation 11. If A and B are m n augmented matrices which are row-equivalent, then the systems they represent are equivalent (i.e., have the same solution set) 12. If A is a m n (coefficient or augmented) matrix over the field K, we call the first nonzero entry in each row its leading entry. EXERCISE #2. Solve 4x + y = -2 2x + 2y + z = 7 2x + y + z = 1 EXERCISE #3. Solve x 1 + x 2 + x 3 + x 4 = 1 x 1 + 2x 2 + x 3 = 0 x 3 + x 4 = 1 x 2 + 2x 3 + x 4 = 1 EXERCISE #4. Solve 4x + y = -2 2x + y + z = 7 2x + y + z = 1 EXERCISE #5. Solve x 1 + x 2 + x 3 + x 4 = 1 x 1 + 2x 2 + x 3 = 0 x 3 + x 4 = 1 x 2 + 2x 3 + 2x 4 = 3 EXERCISE #6. Solve 4x + y = -2 2x + 2y + z = 5 2x + y + z = 1 EXERCISE #7. Solve 4x + y = -2 2x + 2y + z = 1 2x + y + z = 1 Ch. 4 Pg. 12

13 Handout #3 CONNECTION WITH MATRIX Professor Moseley ALGEBRA AND ABSTRACT LINEAR ALGEBRA By using the definition of matrix equality we can think of the system of scalar equations a 11 x 1 + a 12 x a 1n x n = 1 a 21 x 1 + a 22 x a 2n x n = 2 (1) a m1 x 1 + a m2 x a mn x n = m as one vector equation where vector means an n-tuple or column vector. By using matrix multiplication (1) can e written as A x mxn nx1 mx1 where x1 1 a11 a12 a 1n x 2 2 a 21 a 22 a 2n x = [x i ] K n, = [ i ]K m, A = = [a ij ]K mxn. x n m a m1 a m21 a mn If we think of K n and K m as vector spaces, we can define T( x ) = A x (3) mxn nx1 so that T is a mapping from K n to K m. We write T: K n K m. A mapping from a vector space to another vector space is called an operator. We may now view the system of scalar equations as the operator equation: T( x ) = (4) A column vector x K n is a solution to (4) if and only if it is a solution to (1) or (2). Solutions to (4) are just those vectors x in K n that get mapped to in K m y the operator T. The equation T( x ) = 0 (5) is called homogeneous and is the complementary equation to (4). Note that it always has (2) Ch. 4 Pg. 13

14 x = 0 = [0, 0,..., 0] T K n as a solution. x = 0 is called the trivial solution to (5) since it is always a solution. The question is Are there other solutions? and if so How many?. We now have a connection etween solving linear algeraic equations, matrix algera, and astract linear algera. Also we now think of solving linear algeraic equations as an example of a mapping prolem where the operator T:R n R m is defined y (3) aove. We wish to find all solutions to the mapping prolem (4). To introduce this topic, we define what we mean y a linear operator from one vector space to another. DEFINITION #1. An operator T:V W is said to e linear if x, y V and scalars α,β, it is true that T( α x + β y ) = α T( x ) + β T( y ). (6) T: K n K m defined y T( x ) = A x is one example of a linear operator. We will give others mxn nx1 later. To connect the mapping prolem to matrix algera we reveal that if m = n, then (1) (2) and (4) have a unique solution if and only if the matrix A is invertile. THEOREM #1. Suppose m = n so that (1) has the same numer of equations as unknowns. Then (1), (2), and (4) have a unique solution if and only if the matrix A is invertile. EXERCISES on Connection with Matrix Algera and Astract Linear Algera EXERCISE #1. True or False. 1. Since K n and K m are vector spaces, we can define T( x ) = A x so that T is a mxn nx1 mapping from K n to K m. 2. A mapping from a vector space to another vector space is called an operator. 3. Solutions to A x are just those vectors x in K n that get mapped to in K m mxn nx1 mx1 y the operator T( x ) = A x. mxn nx1 4. The equation T( x ) = 0 is called homogeneous and is the complementary equation to T x where T( ) =. mxn nx1 x A x mx1 mxn nx1 5. x = 0 is called the trivial solution to the complementary equation to A x. mxn nx1 mx1 6. An operator T:V W is said to e linear if x, y V and scalars α,β, it is true that T( α x + β y ) = α T( x ) + β T( y ). 7. The operator T: K n to K m defined y T( x ) = A x is a linear operator. mxn nx1 8. A x has a unique solution if and only if the matrix A is invertile. nxn nx1 nx1 Ch. 4 Pg. 14

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