Questions and Answers
What is a Vector Space?
What are the 8 axioms of vector spaces?
- Commutativity of addition, 2. Associativity of addition, 3. Zero vector existence, 4. Each element can be multiplied by -1, 5. For all elements, 1x = x, 6. Associativity of multiplication, 7. Distributive property, 8. (a+b)x = ax + bx.
What is a Zero Matrix?
All entries equal zero.
What is a Subspace?
Signup and view all the answers
The Zero Subspace is a subspace of any vector space V.
Signup and view all the answers
How do you verify a subset, W, as a subspace?
Signup and view all the answers
What is a Symmetric Matrix?
Signup and view all the answers
What is a Diagonal Matrix?
Signup and view all the answers
The zero vector can be considered a linear combination of any subset of V.
Signup and view all the answers
What is a Linear Combination?
Signup and view all the answers
What is the span of a nonempty subset S of vector space V?
Signup and view all the answers
Every linearly independent subset of V can be extended to a basis for V.
Signup and view all the answers
What does it mean for a set to be linearly dependent?
Signup and view all the answers
If V is finite-dimensional, then no linearly independent subset of V can contain more than dim(V) vectors.
Signup and view all the answers
What is the dimension of the vector space {0}?
Signup and view all the answers
What is the dimension of the vector space F^n?
Signup and view all the answers
What is the dimension of the vector space Pn(F)?
Signup and view all the answers
Study Notes
Vector Space
- A vector space is defined by two operations: vector addition and scalar multiplication.
- For any elements x, y in vector space V, x + y is also an element of V (closure under addition).
- For any scalar a from field F and vector x in V, the product ax is an element of V (closure under scalar multiplication).
Axioms of Vector Spaces
- Eight axioms must be satisfied for a set to be a vector space:
- Commutativity of addition: x + y = y + x
- Associativity of addition: (x + y) + z = x + (y + z)
- Existence of zero vector: x + 0 = x
- Existence of additive inverses: x + (-x) = 0
- Multiplicative identity: 1 * x = x
- Associativity of multiplication: (ab)x = a(bx)
- Distributive property: a(x + y) = ax + ay
- Scalar addition: (a + b)x = ax + bx
Subspaces
- A subset W of a vector space V is a subspace if:
- W is also a vector space over F.
- Operations of addition and scalar multiplication in W match those in V.
- The zero subspace is {0}, a subspace in any vector space.
Linear Combinations and Span
- A vector is a linear combination of a subset S if it can be expressed using scalars from F.
- The span of a subset S, denoted span(S), includes all possible linear combinations of vectors in S.
- The span of the empty set is {0}.
Linear Dependence and Independence
- A subset is linearly dependent if there exist scalars, not all zero, such that a linear combination equals zero.
- A subset is linearly independent if the only solution to their linear combination equaling zero is the trivial solution (all coefficients are zero).
- If a set contains the zero vector, it is linearly dependent.
Basis and Dimension
- A basis is a linearly independent subset that spans the vector space.
- The standard basis for F^n consists of unit vectors, like e1 = (1,0,0,...), e2 = (0,1,0,...).
- The dimension of a vector space is the number of vectors in any basis, denoted dim(V).
- Finite-dimensional spaces have a basis consisting of a finite number of vectors; infinite-dimensional spaces do not.
The Replacement Theorem
- If V is spanned by n vectors, and L is a linearly independent set with m vectors, then m ≤ n. Additionally, it is possible to find n-m vectors from the set G such that their union with L spans V.
Transforming a Set into a Basis
- To convert a finite set S into a basis:
- Show it generates V.
- Select a nonzero vector from S.
- Exclude scalar multiples of the chosen vector from the basis.
- Add new vectors iteratively, ensuring linear independence.
Dimension Relations
- The dimension of any subspace W cannot exceed that of vector space V, expressed as dim(W) ≤ dim(V).
- If dim(W) = dim(V), then W and V are the same space.
Matrices Context
- The dimension of a matrix space Mmxn(F) is mn.
- Each matrix entry is determined by its row (m) and column (n) positions.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
Explore the principles of vector spaces, including the two fundamental operations of vector addition and scalar multiplication. This quiz covers the eight axioms that define vector spaces and the concept of subspaces. Test your understanding of these essential linear algebra concepts.
