Vector Spaces and Subspaces
Vector Spaces
A vector is an element of a vector space, which is a fundamental structure in linear algebra. In fact, the study of vector spaces is what we call linear algebra.
Definition 6: Vector Space
A vector space over a field , sometimes called an -vector space, is a set together with a binary operation (called vector addition) and a function (called scalar multiplication) such that the following properties hold:
For all and :
- Associativity of vector addition.
- Commutativity of vector addition.
- Existence of vector additive identity.
- Existence of vector additive inverses.
- Compatibility of field multiplicative identity.
- Distributivity of scalar multiplication over vector addition.
- Distributivity of scalar multiplication over scalar addition.
- Compatibility of scalar and field multiplication.
Together, these are called the vector space axioms. The elements of are called vectors. For any , the element is called the additive inverse of .
Notation
Vectors are often denoted in boldface () or with an arrow on top () to distinguish them from scalars.
Notation
A vector space over a field is usually denoted simply as , where the operations of vector addition and scalar multiplication are implied. When there is ambiguity, the notation and may be used to refer to the vector addition and scalar multiplication operations of , respectively.
Important
The definition of scalar multiplication implies that scalar multiplication is closed over , meaning that for any scalar and any vector , the result of scalar multiplication is also an element of .
Notation
The vector additive identity of a vector space may be denoted when there is ambiguity.
Theorem 3: Multiplication of a vector by the zero scalar
Let be a vector space over a field . Then for any vector , .
Proof: Theorem 3.
Let be a vector space over a field , and let be an arbitrary vector. Call the field additive identity . Then:
Theorem 4: Multiplication of the zero vector by a scalar
Let be a vector space over a field . Then for any scalar , .
Proof: Theorem 4.
Let be a vector space over a field , and let be an arbitrary scalar. Call the vector additive identity . Then:
Theorem 5: Negation is Scalar Multiplication by
Let be a vector space over a field . Then for any vector , .
Proof: Theorem 5.
Let be a vector space over a field , and let be an arbitrary vector. Call the field additive identity and the field multiplicative identity . By the definition of the additive inverse, there exists a scalar such that . Then:
The cartesian product of a field with itself times, denoted , is the set of all -tuples of elements of .
It turns out that for any field and positive integer , is a vector space over under componentwise addition and scalar multiplication.
Notation
In the context of introducing , assume is a positive integer.
Definition 7: The set
Let be a field. The set is the set of all -tuples of elements of :
Definition 8: Operations on
Define componentwise addition and scalar multiplication on as follows:
-
Componentwise addition. For any ,
-
Scalar multiplication. For any scalar and any vector ,
Theorem 6: The set is a vector space over
Let be a field. Then is a vector space over under the operations defined in Definition 8.
Proof: Theorem 6.
Let be a field, and let be the set as defined in Definition 7. Define vector addition and scalar multiplication on as in Definition 8. We will verify that satisfies all vector space axioms under these operations.
Let be arbitrary scalars, and let be arbitrary vectors. Then:
Associativity of vector addition.
Commutativity of vector addition.
Existence of vector additive identity. Choose , where is the additive identity of . For any vector , we have:
Existence of vector additive inverses. For any vector , choose , where is the additive inverse of in . Then:
Compatibility of field multiplicative identity. For any vector :
Distributivity of scalar multiplication over vector addition. For any scalar and any vectors :
Distributivity of scalar multiplication over scalar addition. For any scalars and any vector :
Compatibility of scalar and field multiplication. For any scalars and any vector :
Example.
Example.
Terminology: Real and Complex Vector Spaces
A vector space over is called a real vector space, and a vector space over is called a complex vector space.
Corollary 1
is a real vector space, and is a complex vector space.
Subspaces
A subspace of an -vector space is a subset of which is itself a vector space under the same vector addition and scalar multiplication as .
Definition 9: Subspace
Let be a vector space over a field , and let be a subset of . Let denote the vector addition operation on , and let denote the scalar multiplication function on using scalars from .
Then is a subspace of iff is itself a vector space under the vector addition and scalar multiplication .
Similar to subfields, if we already know that some set is a subset of a vector space , then only three criteria need to be checked (rather than rechecking all axioms):
Theorem 7: Subspace Criteria
Let be a vector space over a field , and let be a subset of . Let denote the vector additive identity of . Then is a subspace of if and only if the following criteria are met:
- Existence of additive identity. .
- Closure under vector addition.
- Closure under scalar multiplication.
Proof: Subspace Criteria.
Suppose is a subset of . Let be the property that is a subspace of , and let be the property that satisfies the three conditions listed in Theorem 7.
. Assume is a subspace of . The vector additive identity of is unique. Since is a subspace of , its vector additive identity must be the same as that of , so . is a vector space under the same operations as , so is closed under vector addition and scalar multiplication. Therefore, is true.
. Assume that , is closed under vector addition, and is closed under scalar multiplication. Then, axiom (3) is satisfied, as well as the requirement that and . Since , the axioms of vector spaces that involve only elements of and the operations and (namely axioms 1, 2, 5, 6, 7, and 8) must also hold for since they hold for all elements of . Finally, axiom (4) is satisfied since for any , we know that by closure of under scalar multiplication, and , so by Theorem 5. Thus, satisfies all vector space axioms under the same operations as , and is a subset of , so is a subspace of . Therefore, is true.
Since and , we have . ∎
Property: is a subspace of every vector space
If is the vector additive identity of a vector space , then is a subspace of . (This is called the trivial subspace of .)
Proof: is a subspace of every vector space.
Suppose is a vector space over , and is the vector additive identity of . Since , it follows that .
- Existence of additive identity.
- Closure under vector addition. For any , we have , so .
- Closure under scalar multiplication. For any scalar and any vector , we have , so .
By Theorem 7, is a subspace of . ∎
Property: Every vector space is a subspace of itself
If is a vector space, then is a subspace of itself, .
Sums of Vector Spaces
Definition 10: Sum of Vector Spaces
Let be vector spaces. The sum is the set of all vectors that can be written as the sum of vectors from each of the vector spaces:
Corollary 2
If are subspaces of a vector space , then is a subspace of .
Example.
Let and be subspaces of . Then:
Theorem 8: Sum of Subspaces is the Smallest Containing Subspace
Let be a vector space, and suppose be subspaces of . Then is the smallest subspace of containing , meaning that if is any subspace of such that for all , then .
Definition 11: Direct Sum of Subspaces
Let be subspaces of a vector space .
- The sum is a direct sum iff each vector in can be written as a unique sum of vectors from each subspace.
- If is a direct sum, we denote it as or .
Example.
Let and be subspaces of . Then:
Nonexample.
Let and be subspaces of . Then:
However, is not a direct sum because the vector can be written as both and .
Corollary 3: Direct Sum of Two Subspaces
Let be a vector space, and let be subspaces of . Then is a direct sum if and only if for some , :
Theorem 9: Formalism for Direct Sum
Let be a vector space, and let be subspaces of . Then is a direct sum if and only if the only way to write as a sum of vectors from each subspace is the trivial combination .
Theorem 10: Direct Sum of Two Subspaces
Let be a vector space, and let be subspaces of . is a direct sum if and only if .
Definition 12: Complement of a Subspace
Let be a vector space, and let be a subspace of . A complement of in the ambient space is a subspace of such that .
Exercises ¶
Exercise 7.
Exercise 8.
Exercise 9.
Exercise 10.
Exercise 11.
Let be the set of all positive real numbers. Define to be a vector space over the field under the following operations:
Show that is in fact a vector space.
Exercise 12.
Exercise 13.
Let be a nonempty set and let be the power set of , i.e. the set of all subsets of . Let be a vector space over the finite field under the operations and defined as follows:
Show that is in fact a vector space.
Exercise 14.
Recall from Exercise 10. Let be a subset of defined by:
Show that is a subspace of .
Exercise 15.
Exercise 16.
Exercise 17.
Exercise 18.
Exercise 19.
Exercise 20.
Exercise 21.
Exercise 22.
Let be the set of all real-valued functions from . Let be the subset of consisting of all even functions, and let be the subset of consisting of all odd functions:
Show that and are subspaces of , and that .