Solutions for Linear Algebra and Its Applications (5th Edition)
Problem 1PP:
Let A = [152031954817], P = [3204], and b = [790]. It can be shown that p is a solution of Ax = b....Problem 2PP:
Let A = [2531], u = [41], and v = [35]. Verify Theorem 5(a) in case by computing A(u + v) and Au +...Problem 3PP:
Construct a 3 3 matrix A and vectors b and c in 3 so that Ax = b has a solution, but Ax = c does...Problem 1E:
Compute the products in Exercises 1-4 using (a) the definition, as in Example 1, and (b) the...Problem 2E:
Compute the products in Exercises 1-4 using (a) the definition, as in Example 1, and (b) the...Problem 3E:
Compute the products in Exercises 1-4 using (a) the definition, as in Example 1, and (b) the...Problem 4E:
Compute the products in Exercises 1-4 using (a) the definition, as in Example 1, and (b) the...Problem 5E:
In Exercises 5-8, use the definition of Ax to write the matrix equation as a vector equation, or...Problem 6E:
In Exercises 5-8, use the definition of Ax to write the matrix equation as a vector equation, or...Problem 7E:
In Exercises 5-8, use the definition of Ax to write the matrix equation as a vector equation, or...Problem 8E:
In Exercises 5-8, use the definition of Ax to write the matrix equation as a vector equation, or...Problem 9E:
In Exercises 9 and 10, write the system first as a vector equation and then as a matrix equation. 9....Problem 10E:
In Exercises 9 and 10, write the system first as a vector equation and then as a matrix equation....Problem 11E:
Given A and b in Exercises 11 and 12, write the augmented matrix for the linear system that...Problem 12E:
Given A and b in Exercises 11 and 12, write the augmented matrix for the linear system that...Problem 13E:
Let u = [044] and A = [352611]. Is u in the plane 3 spanned by the columns of A? (See the figure.)...Problem 14E:
Let u = [232] and A = [587011130]. Is u in the subset of 3 spanned by the columns of A? Why or why...Problem 15E:
Let A = [2163] and b = [b1b2]. Show that the equation Ax = b does not have a solution for all...Problem 16E:
Repeat Exercise 15: A = [134326518], b = [b1b2b3]. 15. Let A = [2163] and b = [b1b2]. Show that the...Problem 17E:
Exercises 17-20 refer to the matrices A and B below. Make appropriate calculations that justify your...Problem 18E:
Exercises 17-20 refer to the matrices A and B below. Make appropriate calculations that justify your...Problem 19E:
Exercises 17-20 refer to the matrices A and B below. Make appropriate calculations that justify your...Problem 20E:
Exercises 17-20 refer to the matrices A and B below. Make appropriate calculations that justify your...Problem 23E:
a. The equation Ax = b is referred to as a vector equation. b. A vector b is a linear combination of...Problem 24E:
a. Every matrix equation Ax = b corresponds to a vector equation with the same solution set. b. Any...Problem 25E:
Note that [431525623][312]=[7310]. Use this fact (and no row operations) to find scalars c1, c2, c3...Problem 26E:
Let u = [725], v = [313], and w = [610]. It can be shown that 3u 5v w = 0. Use this fact (and no...Problem 27E:
Let q1, q2, q3, and v represent vectors in 5, and let x1, x2, and x3 denote scalars. Write the...Problem 28E:
Rewrite the (numerical) matrix equation below in symbolic form as a vector equation, using symbols...Problem 29E:
Construct a 3 3 matrix, not in echelon form, whose columns span 3. Show that the matrix you...Problem 30E:
Construct a 3 3 matrix, not in echelon form, whose columns do not span 3. Show that the matrix you...Problem 31E:
Let A be a 3 2 matrix. Explain why the equation Ax = b cannot be consistent for all b in 3....Problem 32E:
Could a set of three vectors in 4 span all of 4? Explain. What about n vectors in m when n is less...Problem 33E:
Suppose A is a 4 3 matrix and b is a vector in 4 with the property that Ax = b has a unique...Problem 34E:
Suppose A is a 3 3 matrix and b is a vector in 3 with the property that Ax = b has a unique...Problem 35E:
Let A be a 3 4 matrix, let y1 and y2 be vectors in 3, and let w = y1 + y2. Suppose y1 = Ax1 and y2...Problem 36E:
Let A be a 5 3 matrix, let y be a vector in 3, and let z be a vector in 5. Suppose Ay = z. What...Problem 37E:
[M] In Exercises 37-40, determine if the columns of the matrix span 4. 37. [725853496102779215]Browse All Chapters of This Textbook
Chapter 1 - Linear Equations In Linear AlgebraChapter 1.1 - Systems Of Linear EquationsChapter 1.2 - Row Reduction And Echelon FormsChapter 1.3 - Vector EquationsChapter 1.4 - The Matrix Equation Ax = BChapter 1.5 - Solution Sets Of Linear SystemsChapter 1.6 - Applications Of Linear SystemsChapter 1.7 - Linear IndependenceChapter 1.8 - Introduction To Linear TransformationsChapter 1.9 - The Matrix Of A Linear Transformation
Chapter 1.10 - Linear Models In Business, Science, And EngineeringChapter 2 - Matrix AlgebraChapter 2.1 - Matrix OperationsChapter 2.2 - The Inverse Of A MatrixChapter 2.3 - Characterizations Of Invertible MatricesChapter 2.4 - Partitioned MatricesChapter 2.5 - Matrix FactorizationsChapter 2.6 - The Leontief Input-output ModelChapter 2.7 - Applications To Computer GraphicsChapter 2.8 - Subspaces Of R^nChapter 2.9 - Dimension And RankChapter 3 - DeterminantsChapter 3.1 - Introduction To DeterminantsChapter 3.2 - Properties Of DeterminantsChapter 3.3 - Cramer's Rule, Volume, And Linear TransformationsChapter 4 - Vector SpacesChapter 4.1 - Vector Spaces And SubspacesChapter 4.2 - Null Spaces, Column Spaces, And Linear TransformationsChapter 4.3 - Linearly Independent Sets; BasesChapter 4.4 - Coordinate SystemsChapter 4.5 - The Dimension Of A Vector SpaceChapter 4.6 - RankChapter 4.7 - Change Of BasisChapter 4.8 - Applications To Difference EquationsChapter 4.9 - Applications To Markov ChainsChapter 5 - Eigenvalues And EigenvectorsChapter 5.1 - Eigenvectors And EigenvaluesChapter 5.2 - The Characteristic EquationChapter 5.3 - DiagonalizationChapter 5.4 - Eigenvectors And Linear TransformationsChapter 5.5 - Complex EigenvaluesChapter 5.6 - Discrete Dynamical SystemsChapter 5.7 - Applications To Differential EquationsChapter 5.8 - Iterative Estimates For EigenvaluesChapter 6 - Orthogonality And Least SquaresChapter 6.1 - Inner Product, Length, And OrthogonalityChapter 6.2 - Orthogonal SetsChapter 6.3 - Orthogonal ProjectionsChapter 6.4 - The Gram-schmidt ProcessChapter 6.5 - Least-squares ProblemsChapter 6.6 - Applications To Linear ModelsChapter 6.7 - Inner Product SpacesChapter 6.8 - Applications Of Inner Product SpacesChapter 7 - Symmetric Matrices And Quadratic FormsChapter 7.1 - Diagonalization Of Symmetric MatricesChapter 7.2 - Quadratic FormsChapter 7.3 - Constrained OptimizationChapter 7.4 - The Singular Value DecompositionChapter 7.5 - Applications To Image Processing And StatisticsChapter 8.1 - Affine CombinationsChapter 8.2 - Affine IndependenceChapter 8.3 - Convex CombinationsChapter 8.4 - HyperplanesChapter 8.5 - PolytopesChapter 8.6 - Curves And SurfacesChapter 9.1 - Matrix GamesChapter 9.2 - Linear Programming-geometric MethodChapter 9.3 - Linear Programming-simplex MethodChapter 9.4 - DualityChapter 10.1 - Introduction And ExamplesChapter 10.2 - The Steady-state Vector And Google's PagerankChapter 10.3 - Communication ClassesChapter 10.4 - Classification Of States And PeriodicityChapter 10.5 - The Fundamental MatrixChapter 10.6 - Markov Chains And Baseball Statistics
Book Details
Linear algebra is relatively easy for students during the early stages of the course, when the material is presented in a familiar, concrete setting. But when abstract concepts are introduced, students often hit a brick wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations), are not easily understood, and require time to assimilate. Since they are fundamental to the study of linear algebra, students' understanding of these concepts is vital to their mastery of the subject. David Lay introduces these concepts early in a familiar, concrete Rn setting, develops them gradually, and returns to them again and again throughout the text so that when discussed in the abstract, these concepts are more accessible.
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