key term - Basis Vectors

Definition

Basis vectors are a set of linearly independent vectors that form the foundation for representing any vector in a given vector space. They provide a coordinate system that allows vectors to be expressed as a unique linear combination of these basis vectors.

5 Must Know Facts For Your Next Test

1. Basis vectors are the building blocks for representing any vector in a vector space, and they form a coordinate system for that space.
2. The number of basis vectors in a vector space is equal to the dimension of that vector space.
3. Basis vectors are always linearly independent, meaning that no basis vector can be expressed as a linear combination of the others.
4. The coordinates of a vector with respect to a basis represent the unique coefficients of the linear combination of the basis vectors that equal the given vector.
5. Changing the basis of a vector space changes the coordinates of the vectors, but the vectors themselves remain the same.

Review Questions

• Explain the role of basis vectors in representing vectors in a vector space.
  • Basis vectors are the fundamental building blocks for representing any vector in a vector space. They form a coordinate system that allows vectors to be uniquely expressed as a linear combination of the basis vectors. The number of basis vectors is equal to the dimension of the vector space, and they are always linearly independent. This means that any vector in the space can be represented as a unique set of coordinates with respect to the basis vectors.
• Describe how changing the basis of a vector space affects the coordinates of vectors.
  • Changing the basis of a vector space changes the coordinates used to represent the vectors, but the vectors themselves remain the same. When the basis is changed, the coordinates of a vector must be recalculated to express the vector as a linear combination of the new basis vectors. This is because the new basis vectors may have different orientations and magnitudes compared to the previous basis. However, the underlying vector remains unchanged, and its position and direction in the vector space are unaffected by the change in basis.
• Analyze the relationship between the dimension of a vector space and the number of basis vectors required to span that space.
  • The dimension of a vector space is directly related to the number of basis vectors required to span that space. Specifically, the dimension of a vector space is equal to the number of linearly independent basis vectors that can be used to represent any vector in that space. This means that a vector space of dimension $n$ requires $n$ linearly independent basis vectors to form a complete coordinate system for that space. The basis vectors provide the foundation for expressing any vector in the space as a unique linear combination of those basis vectors, with the coefficients of the linear combination serving as the coordinates of the vector.

Related terms

Vector Space:

A vector space is a set of vectors that can be added and multiplied by scalars, satisfying certain properties such as closure and associativity.

Linear Independence:

A set of vectors is linearly independent if none of the vectors in the set can be expressed as a linear combination of the others.

Coordinate System:

A coordinate system is a system that uses a set of numbers, or coordinates, to uniquely identify the position of a point or vector in space.