Solutions for Linear Algebra and Its Applications (5th Edition)
Problem 2PP:
For what value(s) of h will y be in Span{v1, v2, v3} if v1=[112],v2=[547],v3=[310]andy=[43h]Problem 3PP:
Let w1, w2, w3, u, and v be vectors in n. Suppose the vectors u and v are in Span {w1, w2, w3}. Show...Problem 3E:
In Exercises 3 and 4, display the following vectors using arrows on an xy-graph: u, v, v, 2v, u + v....Problem 4E:
In Exercises 3 and 4, display the following vectors using arrows on an xy-graph: u, v, v, 2v, u + v....Problem 5E:
In Exercises 5 and 6, write a system of equations that is equivalent to the given vector equation 5....Problem 6E:
In Exercises 5 and 6, write a system of equations that is equivalent to the given vector equation 6....Problem 7E:
Use the accompanying figure to write each vector listed in Exercises 7 and 8 as a linear combination...Problem 8E:
Use the accompanying figure to write each vector listed in Exercises 7 and 8 as a linear combination...Problem 9E:
In Exercises 9 and 10, write a vector equation that is equivalent to the given system of equations....Problem 10E:
In Exercises 9 and 10, write a vector equation that is equivalent to the given system of equations....Problem 11E:
In Exercises 11 and 12, determine if b is a linear combination of a1, a2,and a3. 11....Problem 12E:
In Exercises 11 and 12, determine if b is a linear combination of a1, a2,and a3. 12....Problem 13E:
In Exercises 13 and 14, determine if b is a linear combination of the vectors formed from the...Problem 14E:
In Exercises 13 and 14, determine if b is a linear combination of the vectors formed from the...Problem 15E:
In Exercises 15 and 16, list five vectors in Span {v1, v2}. For each vector, show the weights on v1...Problem 16E:
In Exercises 15 and 16, list five vectors in Span {v1, v2}. For each vector, show the weights on v1...Problem 17E:
Let a1=[142],a2=[237],andb=[41h]. For what values(s) of h is b in the plane spanned by a1 and a2?Problem 18E:
Let v1=[102],v2=[318],andy=[h53]. For what values(s) of h is y in the plane spanned by v1 and v2?Problem 20E:
Give a geometric description of Span {v1, v2} for the vectors in Exercise 16. 16.v1=[302],v2=[203]Problem 22E:
Construct a 3 3 matrix A, with nonzero entries, and a vector b in 3 such that b is not in the set...Problem 23E:
a. Another notation for the vector [43] is [-4 3]. b. The points in the plane corresponding to [25]...Problem 24E:
a. Any list of five real numbers is a vector in 5. b. The vector u results when a vector u v is...Problem 25E:
Let A = [104032263] and b = [414]. Denote the columns of A by a1, a2, a3, and let W = Span{a1, a2,...Problem 26E:
Let A = [206185121], let b = [1033], let W be the set of all linear combinations of the columns of...Problem 27E:
A mining company has two mines. One days operation at mine #1 produces ore that contains 20 metric...Problem 28E:
A steam plain bums two types of coal: anthracite (A) and bituminous (B). For each ton of A bunted,...Problem 29E:
Let v1, vk be points in 3 and suppose that for j = 1, , k an object with mass mj is located at...Problem 30E:
Let v be the center of mass of a system of point masses located at v1, , vk as in Exercise 29. Is v...Problem 31E:
A thin triangular plate of uniform density and thickness has vertices at v1 = (0, 1), v2 = (8, 1),...Problem 32E:
Consider the vectors v1, v2, v3, and b in 2, shown in the figure. Does the equation x1v1 + x2v2 +...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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