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# Math 1554 - Midterm 1 Study Guide MATH 1554 K3

Georgia Tech

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This 11 page Study Guide was uploaded by awesomenotes on Sunday February 21, 2016. The Study Guide belongs to MATH 1554 K3 at Georgia Institute of Technology taught by Stavros Garoufalidis in Spring 2016. Since its upload, it has received 240 views. For similar materials see Linear Algebra in Mathematics (M) at Georgia Institute of Technology.

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Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 Mark each statement true or false and justify answer. 1. Every elementary row operation is reversible. 2. A 5x6 matrix has six rows. 3. The solution set of a linear system involving variables x ,1,x in a list of numbers (s ,…,s ) that makes each equation in the system a true statement 1 n when the values s ,1,s ane substituted for x ,…1x , nespectively. 4. Two fundamental questions about a linear system involve existence and uniqueness. 5. Elementary row operations on an augmented matrix never change the solution set of the associated linear system. 6. Two matrices are row equivalent if they have the same number of rows. 7. An inconsistent system has more than one solution. 8. Two linear systems are equivalent if they have he same solution set. 9. In some cases, a matrix may be row reduced to more than one matrix in reduced echelon form, using different sequences of row operations. 10.The row reduction algorithm only applies to augmented matrices for a linear system. 11.A basic variable in a linear system is a variable that corresponds to a pivot column in the coefficient matrix. 12.Finding a parametric description of the solution set of a linear system is the same as solving the system. 13.If one row in an echelon form of an augmented matrix is 0 0 0 5 0 , then the associated linear system is inconsistent. 1 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 14.The echelon form of a matrix is unique. 15.The pivot positions in a matrix depend on whether row interchanges are used in the row reduction process. 16.Whenever a system has free variables, the solution set contains many solutions. −4 17.Another notation for the vector is [-4 3]. 3 −2 −5 18.The points in the plane corresponding to 5 and 2 lie on a line through the origin. ▯ 19.An example of a linear combination of vectors v a1d v is 2he vector ???? . ▯ ▯ 20.The solution set of the linear system whose augmented matrix is ???? ▯ ????▯ ???? ▯ ???? is the same as the solution set of the equation ????▯ ▯+ ???? ▯ ▯ ???? ????▯ ▯????. 21.The set Span{u,v} is always visualized as a plane through the origin. 22.Any list of five real numbers is a vector in ℝ . 23.The vector u results when a vector u-v is added to the vector v. 24.The weights c ,1,c ip a linear combination c v1 1+ c v cp pot all be zero. 25.When u and v are nonzero vectors, Span{u,v] contains the line through u and the origin. 26.Asking whether the linear system corresponding to an augmented matrix ???? ▯ ????▯ ???? ▯ ???? has a solution amounts to asking whether b is in Span{a ,a ,a }. 1 2 3 2 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 27.The equation Ax=b is referred to as a vector equation. 28.A vector b is a linear combination of the columns of a matrix A if and only if the equation Ax=b has at least one solution. 29.The equation Ax=b is consistent if the augmented matrix ???? ???? has a pivot position in every row. 30.The first entry in the product Ax is a sum of products. m 31.If the columns of an m x matrix A span ℝ , then the equation Ax=b is m consistent for each b in ℝ . 32.If A is an mxn matrix and if the equation Ax=b is inconsistent for some b in m ℝ , then A cannot have a pivot position in every row. 33.Every matrix equation Ax=b corresponds to a vector equation with the same solution set. 34.Any linear combination of vectors can always be written in the form Ax for a suitable matrix A and vector x 35.The solution set of a linear system whose augmented matrix is ???? ▯ ????▯ ????▯ ???? is the same as the solution set of Ax=b, if ???? = ????▯ ????▯ ????▯ . 36.If the equation Ax=b is inconsistent, then b is not in the set spanned by the columns of A. 37.If the augmented matrix ???? ???? has a pivot position in every row, then the equation Ax=b is inconsistent. m 38.If A is an mxn matrix whose columns do not span ℝ , then the equation Ax=b is inconsistent for some b in ℝ . 39.A homogeneous equation is always consistent. 3 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 40.The equation Ax=0 gives an explicit description of its solution set. 41.The homogenous equation Ax=0 has the trivial solution if and only if the equation has at least one free variable. 42.The equation ???? = ???? + ???????? describes a line through v parallel to p. 43.The solution set of Ax=b is the seet of all vectors of the form ???? = ???? + ???? , ▯ where v is any solution of the equation Ax=0. h 44.If x is a nontrivial solution of Ax=0, then every entry in x is nonzero. 45.The equation ???? = ???? ???? ▯ ???? ????, ▯ith x and2x free v3riables (and neither u or v a multiple of the other), describes a plane through the origin. 46.The equation Ax=b is homogenous is the zero vector is a solution 47. The effect of adding p to a vector is to move the vector in a direction parallel to p. 48.The solution set of Ax=b is obtained by translating the solution set of Ax=0. 49.The columns of a matrix A are linearly independent if the equation Ax=0 had the trivial solution. 50.If S is a linearly independent set, then each vector is a linear combination of the other vectors in S. 51.The columns of a 4x5 matrix are linearly dependent 52.If x and y are linearly independent, and if {x,y,z} is linearly dependent, then z is in Span{x,y}. 53.Two vectors are linearly dependent if and only if they lie on a line through the origin. 4 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 54.If a set contains fewer vectors than there are entries in the vectors, then the set is linearly independent. 55.If x and y are linearly independent, and if z is the Span{x,y}, then {x,y,z} is linearly dependent. ▯ 56.If a set in ℝ is linearly dependent, then the set contains more vectors than there are entries in each vector. 57.A linear transformation is a special type of function. 58.If A is a 3x5 matric and T is a transformation defined by ???? ???? = ????????, then the ▯ domain of T is ℝ . ▯ 59.If A is an m x matrix, the the range of the transformation ???? ⊢ ???????? is ℝ . 60.Every linear transformation is a matrix transformation. 61.A transformation T is linear if ant only if ???? ▯ ▯ + ????▯ ▯ = ????▯???? ???? ▯ + ????▯???? ???? ▯ for all 1 and v 2n the domain of T and for all scalars c an1 c . 2 62.Every matrix transformation is a linear transformation 63.The codomain of the transformation ???? ⊢ ???????? is the set of all linear combinations of the columns of A. ▯ ▯ ▯ 64.If ????:ℝ → ℝ is a linear transformation and if c is in ℝ , then a uniqueness question is “Is c in the range of T?” 65.A linear transformation preserved the operations of vector addition and scalar multiplication 66.The superposition principle is a physical description of a linear transformation. 5 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 67.A linear transformation If ????:ℝ → ℝ ▯ is completely determined by its effect on the columns of the nxn identity matrix. 68.If If ????:ℝ → ℝ rotates vectors about the origin through an angle ????, then T is a linear transformation. 69.When two linear transformations are performed one after another, the combines effect may not always be a linear transformation ▯ ▯ ▯ ▯ 70.A mapping If ????:ℝ → ℝ is onto If ℝ if every vector x in If ℝ maps onto come vector in If ℝ . 71.If A is a 3x2 matrix, then the transformation ???? ⊢ ???????? cannot be one-to-one. 72.Not every linear transformation from ℝ to ℝ ▯ is a matrix transformation 73.The columns of the standard matrix for a linear transformation from ℝ to ℝ ▯ are the images of the columns of the n x identity matrix. ▯ ▯ 74.The standard matrix of a linear transformation from ℝ to ℝ that reflects points ???? 0 through the horizontal axis, the vertical axis, or the origin has the f0rm???? , where a and d are ± 1. 75.A mapping If ????:ℝ → ℝ ▯ is one-to-one if each vector in If ℝ maps onto a ▯ unique vector in If ℝ . 76.If A is a 3x2 matrix, then the transformation ???? ⊢ ???????? cannot map ℝ onto ℝ .▯ 77.If A and B are 2x2 with columns a ,1 a2d b ,b 1 2espectively, then ???????? = ????▯ ▯ ????▯ ▯ . 78.Each column of AB if a linear combination of the columns of B using weights from the corresponding column of A. 79.???????? + ???????? = ????(???? + ????) 6 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 ▯ ▯ ▯ 80.???? + ???? = (???? + ????) 81.The transpose of a product of matrices equals the product of their transposes in the same order. 82.If A and B are 3x3 and ???? = ????▯ ???? ▯ ????▯ , then ???????? = ???????? ▯ ???????? ▯ ???????? ▯. 83.The second row of AB is the second row of A multiplied on the right by B. 84. ???????? ???? = ???????? ???? 85.(????????) = ???? ???? ▯ ▯ 86.The transpose of a sum of matrices equals the sum of their transposes 87.In order for a matrix B to be the inverse of A, both equation ???????? = ???? and ???????? = ???? must be true. ▯▯ ▯▯ 88.If A and B are n nxand invertible, then ???? ???? if the inverse of AB. 89.If ???? = ???? ???? and ???????? − ???????? ≠ 0, then A is invertible. ???? ???? 90.If A is an invertible x n matric, then the equation Ax=b is consistent for each b in ℝ . 91.Each elementary matrix is invertible. 92.A product of invertible n x matrices is invertible, and the inverse of the product is the product of their inverses in the same order. -1 93.If A is invertible, then the inverse of A is A itself. ???? ???? 94.If ???? = ???? ???? and ???????? = ????????, then A is not invertible. 95.If A can be row reduced to the identity matrix, then A must be invertible. 7 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 96.If A is invertible, then elementary row operation that reduce A to the identity I n also reduce A -1 to I . n 97.If the equation Ax=0 has only the trivial solution, then A is row equivalent to the n n identity matrix. x ▯ 98.If the columns of A span ℝ , then the columns ate linearly independent. 99.If A is an n x matrix, then the equation Ax=b has at least one solution for each ▯ b in ℝ . 100. If the equation Ax=0 has a nontrivial solution, then A has fewer than n pivot positions. T 101. If A is not invertible, then A is not invertible. 102. If there is an x n matrix D such that ???????? = ????, there there is also an n x matric C such that ???????? = ????. 103. If the columns of A are linearly independent, then the columns of A ▯ span ℝ . 104. If the equation Ax=b has at least one solution for each b in ℝ , then the solution is unique for each b. ▯ ▯ 105. If the linear transformation ???? ⊢ ???????? maps ℝ into ℝ , then A has n pivot positions. ▯ 106. If there is a b in ℝ such that the equation Ax=b is inconsistent, the the transformation ???? ⊢ ???????? is not one-to-one. ▯ 107. A subspace of ℝ is any set H such that (i) the zero vector is in H, (ii) u, v, and u+v are in H, and (iii) c is a scalar and cu is in H. ▯ 108. If 1 ,…,p are in ℝ , then Span{ v ,1,v }pis the same as the column space of the matrix ????▯ … ???? ▯ 8 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 109. The set of all solutions of a system of m homogeneous equations in n unknowns is a subspace of ℝ . ▯ 110. The columns of an invertible n m matrix form a basis for ℝ ▯ x 111. Row operations do not affect linear dependence relations among the columns of a matrix ▯ 112. A subset of H of ℝ is a subspace if the zero vector is in H. ▯ 113. Given vectors v1,…,vpin ℝ , the set of all linear combinations of these ▯ vectors is a subspace of of ℝ . ▯ 114. The null space of an m nxmatrix is a subspace of of ℝ . 115. The column space of a matrix A is the set of solutions of Ax=b. 116. If B is an echelon form of a matrix A, then the pivot columns of B forma basis for Col A. 117. If ???? = {???? ,…,???? } is a basis for a subspace H and if ???? = ???? ???? + ⋯+ ???? ???? , ▯ ▯ ▯ ▯ ▯ ▯ then ????▯,…,???? ▯re the coordinates of x relative to the basis ????. 118. Each line in ℝ is a one-dimensional subspace of ℝ . ▯ 119. The dimension of Col A is the number of pivot columns of A. 120. The dimensions of Col A and Nul A add up to the number of columns of A. ▯ 121. If a set of p vectors spans a p-dimensional subspace H of ℝ , then these vectors form a basis for H. 122. If ???? is a basis for a subspace H, then each vector in H can be written in only one way as a linear combination of the vectors in ????. 9 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 123. If ???? = {▯ ,…,????▯} is a basis for a subspace H of ℝ , then the ▯ correspondence ???? ⊢ [????] ma▯es H look and act the same as ℝ . 124. The dimension of Nul A is the number of variables in the equation Ax=0. 125. The dimension of the column space of A is rank A. ▯ 126. If H is a p-dimensional subspace of ℝ , then a linearly independent set of p vectors in H is a basis for H. 127. An n n determinant is defined by determinant of ???? − 1 × (???? − 1) x submatrices. 128. The (i,j)-cofactor of a matrix A is the matrix Aijbtained by deleting from A its ith row and jth column. 129. The cofactor expansion of det A down a column is equal to the cofactor expansion along a row. 130. The determinant of a triangular matrix is the sum of the entries on the main diagonal. 131. A row replacement operation does not affect the determinant of a matrix. 132. The determinant of A is the products of the pivots in any echelon form r U of A, multiplied by (-1) , where r is the number of row interchanged made during row reduction from A to U. 133. If the columns of A are linearly dependent, then det A = 0. 134. det ???? + ???? = det???? + det???? 10 Midterm 1 Study Guide Linear Algrebra | Spring 2016 | Chapters 1-3 135. If three row interchanges are made in succession, then the new determinant equals the old determinant. 136. The determinant of A is the product of the diagonal entries in A. 137. If detA is zero, then two rows or two columns are the same, or a row or column is zero. 138. det????▯▯ = −1 det???? 11

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