Solutions for Linear Algebra and Its Applications (5th Edition)
Problem 1PP:
Each of the following equations determines a plane in 3. Do the two planes intersect? If so,...Problem 2PP:
Write the general solution of 10x1 3x2 2x3 = 7 in parametric vector form, and relate the solution...Problem 3PP:
Prove the first pan of Theorem 6: Suppose that p is a solution of Ax = b, so that Ap = b. Let vh, be...Problem 1E:
In Exercises 1-4, determine if the system has a nontrivial solution. Try to use as few row...Problem 2E:
In Exercises 1-4, determine if the system has a nontrivial solution. Try to use as few row...Problem 3E:
In Exercises 1-4, determine if the system has a nontrivial solution. Try to use as few row...Problem 4E:
In Exercises 1-4, determine if the system has a nontrivial solution. Try to use as few row...Problem 5E:
In Exercises 5 and 6, follow the method of Examples 1 and 2 to write the solution set of the given...Problem 6E:
In Exercises 5 and 6, follow the method of Examples 1 and 2 to write the solution set of the given...Problem 7E:
In Exercises 7-12, describe all solutions of Ax = 0 in parametric vector form, where A is row...Problem 8E:
In Exercises 7-12, describe all solutions of Ax = 0 in parametric vector form, where A is row...Problem 9E:
In Exercises 7-12, describe all solutions of Ax = 0 in parametric vector form, where A is row...Problem 10E:
In Exercises 7-12, describe all solutions of Ax = 0 in parametric vector form, where A is row...Problem 11E:
In Exercises 7-12, describe all solutions of Ax = 0 in parametric vector form, where A is row...Problem 12E:
In Exercises 7-12, describe all solutions of Ax = 0 in parametric vector form, where A is row...Problem 13E:
Suppose the solution set of a certain system of linear equations can be described as x1 = 5 + 4x3,...Problem 14E:
Suppose the solution set of a certain system of linear equations can be described as x1 = 3x4, x2 =...Problem 15E:
Follow the method of Example 3 to describe the solutions of the following system in parametric...Problem 16E:
As in Exercise 15, describe the solutions of the following system in parametric vector form, and...Problem 19E:
In Exercises 19 and 20, find the parametric equation of the line through a parallel to b. 19. a =...Problem 20E:
In Exercises 19 and 20, find the parametric equation of the line through a parallel to b. 20. a =...Problem 21E:
In Exercises 21 and 22, find a parametric equation of the line M through p and q. [Hint: M is...Problem 22E:
In Exercises 21 and 22, find a parametric equation of the line M through p and q. [Hint: M is...Problem 23E:
a. A homogeneous equation is always consistent. b. The equation Ax = 0 gives an explicit description...Problem 24E:
a. If x is a nontrivial solution of Ax = 0, then every entry in x is nonzero. b. The equation x =...Problem 25E:
Prove the second part of Theorem 6: Let w be any solution of Ax = b, and define vh = w p. Show that...Problem 26E:
Suppose Ax = b has a solution. Explain why the solution is unique precisely when Ax = 0 has only the...Problem 27E:
Suppose A is the 3 3 zero matrix (with all zero Describe the solution set of the equation Ax = 0.Problem 29E:
In Exercises 29-32, (a) does the equation Ax = 0 have a nontrivial solution and (b) does the...Problem 30E:
In Exercises 29-32, (a) does the equation Ax = 0 have a nontrivial solution and (b) does the...Problem 31E:
In Exercises 29-32, (a) does the equation Ax = 0 have a nontrivial solution and (b) does the...Problem 32E:
In Exercises 29-32, (a) does the equation Ax = 0 have a nontrivial solution and (b) does the...Problem 33E:
Given A = [2672139], find one nontrivial solution of Ax = 0 by inspection. [Hint: Think of the...Problem 37E:
Construct a 2 2 matrix A such that the solution set of the equation Ax = 0 is the line in 2 through...Problem 38E:
Suppose A is a 3 3 matrix and y is a vector in 3 such that the equation Ax = y does not have a...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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