7: Vector Spaces
( \newcommand{\kernel}{\mathrm{null}\,}\)
- 7.1: Vector Space - Definition
- This section introduces the abstract framework of vector spaces, extending beyond the familiar geometrical interpretation. A vector space is defined as a set of elements, called vectors, equipped with two operations including vector addition and scalar multiplication that satisfy a specific set of axioms.
- 7.2: Vector Space - Examples
- This page analyzes the verification of vector space properties for the set of polynomials of degree at most 2 (P2) and a function space (FS). It establishes closure under addition and scalar multiplication, confirms commutativity and associativity, identifies the zero function as the additive identity, and demonstrates the existence of additive inverses.
- 7.3: Spanning Sets
- In this section we will examine the concept of spanning introduced earlier in terms of Rn . Here, we will discuss these concepts in terms of abstract vector spaces.
- 7.4: Linear Independence
- In this section, we will again explore concepts introduced earlier in terms of Rn and extend them to apply to abstract vector spaces.
- 7.5: Subspaces
- In this section we will examine the concept of subspaces introduced earlier in terms of Rn. Here, we will discuss these concepts in terms of abstract vector spaces.
- 7.6: Basis
- In this section we extend a linearly independent set and shrink a spanning set to a basis of a given vector space.
- 7.7: Sums and Intersections
- In this section we discuss sum and intersection of two subspaces.
- 7.8: Linear Transformations
- In this section we discuss the definition of a linear transformation in the context of vector spaces.
- 7.9: Isomorphisms
- This page explores linear transformations in vector spaces, focusing on one-to-one and onto transformations and their role in defining isomorphisms. It establishes that a linear transformation T is an isomorphism if it is both one-to-one and onto, retaining linear independence of bases.
- 7.10: The Kernel and Image of a Linear Map
- Here we consider the case where the linear map is not necessarily an isomorphism. First here is a definition of what is meant by the image and kernel of a linear transformation.
- 7.11: The Matrix of a Linear Transformation
- You may recall from Rn that the matrix of a linear transformation depends on the bases chosen. This concept is explored in this section, where the linear transformation now maps from one arbitrary vector space to another.
- 7.12: Inner Product Spaces
- The dot product was introduced in Rn to provide a natural generalization of the geometrical notions of length and orthogonality. The plan in this section is to define an inner product on an arbitrary real vector space V (of which the dot product is an example in Rn ) and use it to introduce these concepts in V.