Label the following statements as true or false. (a) Every vector space contains a zero vector. (b) A vector space may have more than one zero vector. (c) In any vector space, ax = bx implies that a = 6. (d) In any vector space, ax = ay implies that x = y. (e) A vector in F" may be regarded as a matrix in M.xi(F). (f) An m x n matrix has m columns and n rows. (g) In P(F). only polynomials of the same degree may be added. (h) If / and o are polynomials of degree n, then / + g is a polynomial of degree n. (i) If / is a polynomial of degree n and c is a nonzero scalar, then cf is a polynomial of degree n. Sec. 1.2 Vector Spaces 13 (j) A nonzero scalar of F may be considered to be a polynomial in P(F) having degree zero, (k) Two functions in F(S, F) are equal if and only if they have the same value at each element of S.
Read moreTable of Contents
`6.10
Inner Products and Norms
1.1
Introduction
1.2
Vector Spaces
1.3
Subspaces
1.4
Linear Combinations and Systems of Linear Equations
1.5
Linear Dependence and Linear Independence
1.6
Bases and Dimension
1.7
Maximal Linearly Independent Subsets
2.1
Linear Transformations. Null Spaces, and Ranges
2.2
The Matrix Representation of a Linear Transformation
2.3
Composition of Linear Transformations and Matrix Multiplication
2.4
Invertibility and Isomorphisms
2.5
The Change of Coordinate Matrix
2.6
Dual Spaces
2.7
Homogeneous Linear Differential Equations with Constant Coefficients
3.1
Elementary Matrix Operations and Elementary Matrices
3.2
The Rank of a Matrix and Matrix Inverses
3.3
Systems of Linear Equations Theoretical Aspects
3.4
Systems of Linear Equations Computational Aspects
4.1
Determinants of Order 2
4.2
Determinants of Order n
4.3
Properties of Determinants
4.4
Summary Important Facts about Determinants
4.5
A Characterization of the Determinant
5.1
Eigenvalues and Eigenvectors
5.2
Diagonalizability
5.3
Matrix Limits and Markov Chains
5.4
Invariant Subspaces and the Cayley Hamilton Theorem
6.1
Inner Products and Norms
6.10
Inner Products and Norms
6.11
The Geometry of Orthogonal Operators
6.2
The Gram-Schmidt Orthogonalization Process and Orthogonal Complements
6.3
The Adjoint of a Linear Operator
6.4
Normal and Self-Adjoint. Operators
6.5
Unitary and Orthogonal Operators and Their Matrices
6.6
Orthogonal Projections and the Spectral Theorem
6.7
The Singular Value Decomposition and the Pseudoinverse
6.8
Bilinear and Quadratic Forms
6.9
Einstein As Special Theory of Relativity
7.1
The Jordan Canonical Form I
7.2
The Jordan Canonical Form II
7.3
The Minimal Polynomial
7.4
The Rational Canonical Form
Textbook Solutions for Linear Algebra
Chapter 1.2 Problem 10
Question
Let V denote the set of all differentiablc real-valued functions defined on the real line. Prove that V is a vector space with the operations of addition and scalar multiplication defined in Example 3.
Solution
Step 1 of 7
Let V denote the set of all differentiable real-valued functions defined on the real line.
To prove that V is a vector space with the operations of addition and scalar multiplication defined as
For
Commutativity of Addition: -
Since the sum of differentiable functions is again differentiable and V is the set of all differentiable real-valued functions, therefore, the addition is commutative.
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Title
Linear Algebra 4
Author
Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence
ISBN
9780130084514