# 12. x 1 = 4x 1 + 3x 2 + 4t, x 2 = 6x 1 4x 2 + t x 1 = t2 x 1 tx 2, x 2 = ( sin t)x 1 + x x 1 = e2t x 2, x 2 + (sin t)x 1 = 1.

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1 page Verify that for all values of t, (1 t,2 + 3t,3 2t) is a solution to the linear system x 1 + x 2 + x 3 = 6, x 1 x 2 2x 3 = 7, 5x 1 + x 2 x 3 = 4 24 Elementary Row Operations and Row-Echelon Matrices A = 4 1 2, b = Consider the m n homogeneous system of linear equations Ax = 0 (232) 4 Verify that for all values of s and t, (s, s 2t,2s + 3t,t) is a solution to the linear system x 1 + x 2 x 3 + 5x 4 = 0, 2x 2 x 3 + 7x 4 = 0, 4x 1 + 2x 2 3x x 4 = 0 5 By making a sketch in the xy-plane, prove that the following linear system has no solution: 2x + 3y = 1, 2x + 3y = 2 For Problems 6 8, determine the coefficient matrix, A, the right-hand-side vector, b, and the augmented matrix A # of the given system x 1 + 2x 2 3x 3 = 1, 2x 1 + 4x 2 5x 3 = 2, 7x 1 + 2x 2 x 3 = 3 x + y + z w = 3, 2x + 4y 3z + 7w = 2 x 1 + 2x 2 x 3 = 0, 2x 1 + 3x 2 2x 3 = 0, 5x 1 + 6x 2 5x 3 = 0 For Problems 9 10, write the system of equations with the given coefficient matrix and right-hand-side vector A = , b = (a) If x =[x 1 x 2 x n ] T and y =[y 1 y 2 y n ] T are solutions to (232), show that z = x + y and w = cx are also solutions, where c is an arbitrary scalar (b) Is the result of (a) true when x and y are solutions to the nonhomogeneous system Ax = b? Explain For Problems 12 15, write the vector formulation for the given system of differential equations 12 x 1 = 4x 1 + 3x 2 + 4t, x 2 = 6x 1 4x 2 + t 2 13 x 1 = t2 x 1 tx 2, x 2 = ( sin t)x 1 + x 2 14 x 1 = e2t x 2, x 2 + (sin t)x 1 = 1 15 x 1 = ( sin t)x 2 + x 3 + t, x 2 = et x 1 + t 2 x 3 + t 3, x 3 = tx 1 + t 2 x For Problems verify that the given vector function x defines a solution to x = Ax + b for the given A and b [ e 4t 16 x(t) = 2e 4t ], A = [ 4e 17 x(t) = 2t ] + 2 sin t 3e 2t, A = cos t [ ] 2(cos t + sin t) b(t) = 7sint + 2 cos t [ ] [ ] 2 1 0, b(t) = [ ] 1 4, Elementary Row Operations and Row-Echelon Matrices In the next section we will develop methods for solving a system of linear equations These methods will consist of reducing a given system of equations to a new system that has the same solution set as the given system but is easier to solve In this section we introduce the requisite mathematical results

2 page CHAPTER 2 Matrices and Systems of Linear Equations Elementary Row Operations The first step in deriving systematic procedures for solving a linear system is to determine what operations can be performed on such a system without altering its solution set Example 241 Consider the system of equations x 1 + 2x 2 + 4x 3 = 2, (241) 2x 1 5x 2 + 3x 3 = 6, (242) 4x 1 + 6x 2 7x 3 = 8 (243) Solution: If we permute (ie, interchange), say, Equations (241) and (242), the resulting system is 2x 1 5x 2 + 3x 3 = 6, x 1 + 2x 2 + 4x 3 = 2, 4x 1 + 6x 2 7x 3 = 8, which certainly has the same solution set as the original system Returning to the original system, if we multiply, say, Equation (242) by 5, we obtain the system x 1 + 2x 2 + 4x 3 = 2, 10x 1 25x x 3 = 30, 4x 1 + 6x 2 7x 3 = 8, which again has the same solution set as the original system Finally, if we add, say, twice Equation (241) to Equation (243), we obtain the system x 1 + 2x 2 + 4x 3 = 2, (244) 2x 1 5x 2 + 3x 3 = 6, (245) (4x 1 + 6x 2 7x 3 ) + 2(x 1 + 2x 2 + 4x 3 ) = 8 + 2(2) (246) We can verify that, if (244) (246) are satisfied, then so are (241) (243), and vice versa It follows that the system of equations (244) (246) has the same solution set as the original system of equations (241) (243) More generally, similar reasoning can be used to show that the following three operations can be performed on any m n system of linear equations without altering the solution set: 1 Permute equations 2 Multiply an equation by a nonzero constant 3 Add a multiple of one equation to another equation Since these operations involve changes only in the system coefficients and constants (and not changes in the variables), they can be represented by the following operations on the rows of the augmented matrix of the system: 1 Permute rows 2 Multiply a row by a nonzero constant 3 Add a multiple of one row to another row

3 page Elementary Row Operations and Row-Echelon Matrices 141 These three operations, called elementary row operations, will be a basic computational tool throughout the text, even in cases when the matrix under consideration is not derived from a system of linear equations The following notation will be used to describe elementary row operations performed on a matrix A 1 P ij : Permute the ith and jth rows in A 2 M i (k): Multiply every element of the ith row of A by a nonzero scalar k 3 A ij (k): Add to the elements of the jth row of A the scalar k times the corresponding elements of the ith row of A Furthermore, the notation A B will mean that matrix B has been obtained from matrix A by a sequence of elementary row operations To reference a particular elementary row operation used in, say, the nth step of the sequence of elementary row operations, we will write n B Example 242 The one-step operations performed on the system in Example 241 can be described as follows using elementary row operations on the augmented matrix of the system: P 12 Permute (241) and (242) M 2 (5) Multiply (242) by A 13 (2) Add 2 times (241) to (243) It is important to realize that each elementary row operation is reversible; we can undo a given elementary row operation by another elementary row operation to bring the modified linear system back into its original form Specifically, in terms of the notation introduced above, the reverse operations are determined as follows (ERO refers here to elementary row operation ): ERO Applied to A A B Reverse ERO Applied to B B A P ij P ji : Permute row j and i in B M i (k) M i (1/k): Multiply the ith row of B by 1/k A ij (k) A ij ( k): Add to the elements of the jth row of B the scalar k times the corresponding elements of the ith row of B We introduce a special term for matrices that are related via elementary row operations DEFINITION 243 Let A be an m n matrix Any matrix obtained from A by a finite sequence of elementary row operations is said to be row-equivalent to A

4 page CHAPTER 2 Matrices and Systems of Linear Equations Thus, all of the matrices in the previous example are row-equivalent Since elementary row operations do not alter the solution set of a linear system, we have the next theorem Theorem 244 Systems of linear equations with row-equivalent augmented matrices have the same solution sets Row-Echelon Matrices Our methods for solving a system of linear equations will consist of using elementary row operations to reduce the augmented matrix of the given system to a simple form But how simple a form should we aim for? In order to answer this question, consider the system x 1 + x 2 x 3 = 4, (247) x 2 3x 3 = 5, (248) x 3 = 2 (249) This system can be solved most easily as follows From Equation (249), x 3 = 2 Substituting this value into Equation (248) and solving for x 2 yields x 2 = = 11 Finally, substituting for x 3 and x 2 into Equation (247) and solving for x 1, we obtain x 1 = 5 Thus, the solution to the given system of equations is ( 5, 11, 2), a single vector in R 3 This technique is called back substitution and could be used because the given system has a simple form The augmented matrix of the system is We see that the submatrix consisting of the first three columns (which corresponds to the matrix of coefficients) is an upper triangular matrix with the leftmost nonzero entry in each row equal to 1 The back-substitution method will work on any system of linear equations with an augmented matrix of this form Unfortunately, not all systems of equations have augmented matrices that can be reduced to such a form However, there is a simple type of matrix to which any matrix can be reduced by elementary row operations, and which also represents a system of equations that can be solved (if it has a solution) by back substitution This is called a row-echelon matrix and is defined as follows: DEFINITION 245 An m n matrix is called a row-echelon matrix if it satisfies the following three conditions: 1 If there are any rows consisting entirely of zeros, they are grouped together at the bottom of the matrix 2 The first nonzero element in any nonzero row 4 is a 1 (called a leading 1) 3 The leading 1 of any row below the first row is to the right of the leading 1 of the row above it 4 A nonzero row (nonzero column) is any row (column) that does not consist entirely of zeros

5 page Elementary Row Operations and Row-Echelon Matrices 143 Example 246 Examples of row-echelon matrices are , , 000 and , whereas and are not row-echelon matrices The basic result that will allow us to determine the solution set to any system of linear equations is stated in the next theorem Theorem 247 Example 248 Any matrix is row-equivalent to a row-echelon matrix According to this theorem, by applying an appropriate sequence of elementary row operations to any m n matrix, we can always reduce it to a row-echelon matrix When a matrix A has been reduced to a row-echelon matrix in this way, we say that it has been reduced to row-echelon form and refer to the resulting matrix as a row-echelon form of A The proof of Theorem 247 consists of giving an algorithm that will reduce an arbitrary m n matrix to a row-echelon matrix after a finite sequence of elementary row operations Before presenting such an algorithm, we first illustrate the result with an example Use elementary row operations to reduce to row-echelon form Solution: We show each step in detail Step 1: Put a leading 1 in the (1, 1) position This is most easily accomplished by permuting rows 1 and Step 2: Use the leading 1 to put zeros beneath it in column 1 This is accomplished by adding appropriate multiples of row 1 to the remaining rows Step 2 row operations: Add 2 times row 1 to row 2 Add 4 times row 1 to row 3 Add 2 times row 1 to row 4 Step 3: Put a leading 1 in the (2, 2) position We could accomplish this by multiplying row 2 by 1/3 However, this would introduce fractions into the matrix and thereby complicate the remaining computations In

7 page Elementary Row Operations and Row-Echelon Matrices The reader may have noticed that the particular steps taken in the preceding example are not uniquely determined For instance, we could have achieved a leading 1 in the (1, 1) position in step 1 by multiplying the first row by 1/2, rather than permuting the first two rows Therefore, we may have multiple strategies for reducing a matrix to row-echelon form, and indeed, many possible row-echelon forms for a given matrix A In this particular case, we chose not to multiply the first row by 1/2 in order to avoid introducing fractions into the calculations The reader is urged to study the foregoing example very carefully, since it illustrates the general procedure for reducing an m n matrix to row-echelon form using elementary row operations This procedure will be used repeatedly throughout the text The idea behind reduction to row-echelon form is to start at the upper left-hand corner of the matrix and proceed downward and to the right in the matrix The following algorithm formalizes the steps that reduce any m n matrix to row-echelon form using a finite number of elementary row operations and thereby provides a proof of Theorem 247 An illustration of this algorithm is given in Figure 241 Algorithm for Reducing an m n Matrix A to Row-Echelon Form 1 Start with an m n matrix A IfA = 0, go to step 7 2 Determine the leftmost nonzero column (this is called a pivot column, and the topmost position in this column is called a pivot position) 3 Use elementary row operations to put a 1 in the pivot position 4 Use elementary row operations to put zeros below the pivot position 5 If there are no more nonzero rows below the pivot position go to step 7, otherwise go to step 6 6 Apply steps 2 through 5 to the submatrix consisting of the rows that lie below the pivot position 7 The matrix is a row-echelon matrix 0 0 zeros Submatrix Elementary row Elementary row operations applied operations applied to pivot column to rows beneath 0 0 in submatrix pivot position ~ ~ Pivot position Nonzero elements Pivot column zeros zeros New submatrix Figure 241: Illustration of an algorithm for reducing an m n matrix to row-echelon form Example 249 Remark In order to obtain a row-echelon matrix, we put a1ineach pivot position However, many algorithms for solving systems of linear equations numerically are based around the preceding algorithm, except that in step 3 we place a nonzero number (not necessarily a 1) in the pivot position Of course, the matrix resulting from an application of this algorithm differs from a row-echelon matrix, since it will have arbitrary nonzero elements in the pivot positions Reduce to row-echelon form

8 page CHAPTER 2 Matrices and Systems of Linear Equations Solution: Applying the row-reduction algorithm leads to the following sequence of elementary row operations The specific row operations used at each step are given at the end of the process Pivot position Pivot column Pivot column Pivot column Pivot position Pivot position This is a row-echelon matrix and hence we are done The row operations used are summarized here: 1 P 12 2 A 12 ( 3), A 13 ( 1) 3 M 2 ( 1) 4 A 23 (1) 5 M 3 (1/4) The Rank of a Matrix We now derive some further results on row-echelon matrices that will be required in the next section to develop the theory for solving systems of linear equations We first observe that a row-echelon form for a matrix A is not unique Given one row-echelon form for A, we can always obtain a different one by taking the first rowechelon form for A and adding some multiple of a given row to any rows above it The result is still in row-echelon form However, even though the row-echelon form of A is not unique, we do have the following theorem (in Chapter 4 we will see how the proof of this theorem arises naturally from the more sophisticated ideas from linear algebra yet to be introduced) Theorem 2410 Let A be an m n matrix All row-echelon matrices that are row-equivalent to A have the same number of nonzero rows Theorem 2410 associates a number with any m n matrix A namely, the number of nonzero rows in any row-echelon form of A As we will see in the next section, this number is fundamental in determining the solution properties of linear systems, and indeed it plays a central role in linear algebra in general For this reason, we give it a special name DEFINITION 2411 The number of nonzero rows in any row-echelon form of a matrix A is called the rank of A and is denoted rank(a) Example 2412 Determine rank(a)ifa =

9 page Elementary Row Operations and Row-Echelon Matrices 147 Solution: In order to determine rank(a), we must first reduce A to row-echelon form Since there are two nonzero rows in this row-echelon form of A, it follows from Definition 2411 that rank(a)=2 1 A 31 ( 1) 2 A 12 ( 4), A 13 ( 2) 3 M 2 ( 1/5) 4 A 23 (5) In the preceding example, the original matrix A had three nonzero rows, whereas any row-echelon form of A has only two nonzero rows We can interpret this geometrically as follows The three row vectors of A can be considered as vectors in R 3 with components a 1 = (3, 1, 4), a 2 = (4, 3, 5), a 3 = (2, 1, 3) In performing elementary row operations on A, we are taking combinations of these vectors in the following way: c 1 a 1 + c 2 a 2 + c 3 a 3, and thus the rows of a row-echelon form of A are all of this form We have been combining the vectors linearly The fact that we obtained a row of zeros in the row-echelon form means that there are values of the constants c 1,c 2,c 3 such that c 1 a 1 + c 2 a 2 + c 3 a 3 = 0, where 0 denotes the zero vector (0, 0, 0) Equivalently, one of the vectors can be written in terms of the other two vectors, and therefore the three vectors lie in a plane Reducing the matrix to row-echelon form has uncovered this relationship among the three vectors We shall have much more to say about this in Chapter 4 Remark If A is an m n matrix, then rank(a) m and rank(a) n This is because the number of nonzero rows in a row-echelon form of A is equal to the number of pivots in a row-echelon form of A, which cannot exceed the number of rows or columns of A, since there can be at most one pivot per row and per column Reduced Row-Echelon Matrices In the future we will need to consider the special row-echelon matrices that arise when zeros are placed above, as well as beneath, each leading 1 Any such matrix is called a reduced row-echelon matrix and is defined precisely as follows DEFINITION 2413 An m n matrix is called a reduced row-echelon matrix if it satisfies the following conditions: 1 It is a row-echelon matrix 2 Any column that contains a leading 1 has zeros everywhere else

10 page CHAPTER 2 Matrices and Systems of Linear Equations Example 2414 The following are examples of reduced row-echelon matrices: , 0001 [ ] , , 0000 and Although an m n matrix A does not have a unique row-echelon form, in reducing A to a reduced row-echelon matrix we are making a particular choice of row-echelon matrix, since we arrange that all elements above each leading 1 are zeros In view of this, the following theorem should not be too surprising: Theorem 2415 An m n matrix is row-equivalent to a unique reduced row-echelon matrix The unique reduced row-echelon matrix to which a matrix A is row-equivalent will be called the reduced row-echelon form of A As illustrated in the next example, the row-reduction algorithm is easily extended to determine the reduced row-echelon form of A we just put zeros above and beneath each leading Example 2416 Determine the reduced row-echelon form of A = Solution: We apply the row-reduction algorithm, but put 0s above and below the leading 1s In so doing, it is immaterial whether we first reduce A to row-echelon form and then arrange 0s above the leading 1s, or arrange 0s both above and below the leading 1s as we proceed from left to right A = which is the reduced row-echelon form of A 1 A 21 (2) 2 A 12 (1), A 13 ( 2) 3 M 2 (1/14) 4 A 21 ( 9), A 23 (14) Exercises for 24 Key Terms Elementary row operations, Row-equivalent matrices, Back substitution, Row-echelon matrix, Row-echelon form, Leading 1, Pivot, Rank of a matrix, Reduced row-echelon matrix Skills Be able to perform elementary row operations on a matrix Be able to determine a row-echelon form or reduced row-echelon form for a matrix Be able to find the rank of a matrix True-False Review For Questions 1 9, decide if the given statement is true or false, and give a brief justification for your answer If true, you can quote a relevant definition or theorem from the text If false, provide an example, illustration, or brief explanation of why the statement is false 1 A matrix A can have many row-echelon forms but only one reduced row-echelon form

11 page Elementary Row Operations and Row-Echelon Matrices Any upper triangular n n matrix is in row-echelon 0100 form Any n n matrix in row-echelon form is upper triangular 4 If a matrix A has more rows than a matrix B, then rank(a) rank(b) 5 For any matrices A and B of the same dimensions, rank(a + B) = rank(a) + rank(b) 6 For any matrices A and B of the appropriate dimensions, rank(ab) = rank(a) rank(b) 7 If a matrix has rank zero, then it must be the zero matrix 8 The matrices A and 2A must have the same rank 9 The matrices A and 2A must have the same reduced row-echelon form Problems For Problems 1 8, determine whether the given matrices are in reduced row-echelon form, row-echelon form but not reduced row-echelon form, or neither [ ] [ ] [ ] For Problems 9 18, use elementary row operations to reduce the given matrix to row-echelon form, and hence determine the rank of each matrix [ ] [ ] For Problems 19 25, reduce the given matrix to reduced rowechelon form and hence determine the rank of each matrix [ ]

12 page CHAPTER 2 Matrices and Systems of Linear Equations the linear algebra package of Maple, the three elementary row operations are 12 1 swaprow(a,i,j): permute rows i and j mulrow(a,i,k): multiply row i by k addrow(a,i,j): add k times row i to row j For Problems 26 28, use some form of technology to determine a row-echelon form of the given matrix The matrix in Problem The matrix in Problem The matrix in Problem Many forms of technology also have built-in functions for directly determining the reduced row-echelon form of a given matrix A For example, in the linear algebra package of Maple, the appropriate command is rref(a) In Prob lems 29 31, use technology to determine directly the reduced 0121 row-echelon form of the given matrix Many forms of technology have commands for performing elementary row operations on a matrix A For example, in 29 The matrix in Problem The matrix in Problem The matrix in Problem Gaussian Elimination We now illustrate how elementary row-operations applied to the augmented matrix of a system of linear equations can be used first to determine whether the system is consistent, and second, if the system is consistent, to find all of its solutions In doing so, we will develop the general theory for linear systems of equations Example 251 Determine the solution set to 3x 1 2x 2 + 2x 3 = 9, x 1 2x 2 + x 3 = 5, 2x 1 x 2 2x 3 = 1 (251) Solution: We first use elementary row operations to reduce the augmented matrix of the system to row-echelon form

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