Label the following statements as true or false. (a) If S is a linearly dependent set, then each vector in S is a linear combination of other vectors in S. (b) Any set containing the zero vector is linearly dependent. (c) The empty set is linearly dependent. (d) Subsets of linearly dependent sets are linearly dependent. (e) Subsets of linearly independent sets are linearly independent. (f) If aixi 4- a2x2 4- 4- anxn = 0 and Xi,x2 ,... ,xn are linearly independent, then all the scalars a are zero.
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 1.5 Problem 7
Question
Recall from Example 3 in Section 1.3 that the set of diagonal matrices in \(\mathrm{M}_{2 \times 2}(F)\) is a subspace. Find a linearly independent set that generates this subspace.
Solution
Step 1 of 3
Given data:
The set of diagonal matrices in \({{\rm{M}}_{{\rm{2 \times 2}}}}\left( {\rm{F}} \right)\) is a subspace.
Linearly independent:
A set of vectors is linearly independent if the only linear combination of the vectors is equal to zero is the trivial linear combination.
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full solution
Title
Linear Algebra 4
Author
Stephen H. Friedberg, Arnold J. Insel, Lawrence E. Spence
ISBN
9780130084514