Linear algebra; conditions for spaces

In summary, the conditions for the vectors in a basis are that they must be linearly independent and span the required space. For a basis of R^3, the vectors must also be orthogonal. However, for a subspace of R^3, the vectors only need to be linearly independent. For a symmetric matrix A, there exists an orthogonal matrix S that satisfies S^(-1) *D*S, where D is a diagonal matrix consisting of eigenvalues. However, for a diagonalizable matrix B that is not symmetric, this is not necessarily true.
  • #1
Niles
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[SOLVED] Linear algebra; conditions for spaces

Homework Statement


1) If I want to write a basis for R^3, what must the conditions for the three vectors? Must they be linearly independant or orthogonal or what?

2) If I want to write a basis for a supspace of R^3, what must the conditions for the vectors be? Must they only be linearly independant?

3) If I have a symmetric matrix A, I can find an orthogonal matrix S that satisfies S^(-1) *D*S, where D is a diagonal matrix consisting of eigenvalues.

If I have a diagonalizable matrix B, which is not symmetric, can I also find an orthogonal matrix S that satisfies what I wrote above?

The Attempt at a Solution


For the vectors spanning R^3, I believe they have to be orthogonal and linearly independant.

For the vectors spanning a subspace of R^3, I believe the vectors just have to be linearly independant. Correct?
 
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  • #2
The conditions for the vectors in a basis is that they be linearly independent and span the required space. They don't have to be orthogonal unless the the problem says 'orthogonal basis'. Symmetric real matrices have the special property that eigenvectors corresponding to different eigenvalues are orthogonal. That's why you can construct an orthogonal matrix S. For a general diagonalizable matrix B, this is not true.
 
  • #3
Cool, thanks :-)
 

FAQ: Linear algebra; conditions for spaces

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors, along with operations such as addition and scalar multiplication. These operations follow specific rules, and the set of vectors must satisfy certain properties in order to be considered a vector space.

2. What are the conditions for a set of vectors to form a vector space?

In order for a set of vectors to form a vector space, they must satisfy the following conditions:

  • The vectors must be closed under addition and scalar multiplication.
  • The vectors must have a zero vector, which is the vector that behaves as the identity element for addition.
  • The vectors must be associative and commutative under addition.
  • The vectors must have an inverse element for addition.
  • The vectors must satisfy the distributive property for scalar multiplication over addition.

3. What is a subspace?

A subspace is a subset of a vector space that also satisfies the conditions for a vector space. This means that all vectors in the subspace are also in the original vector space, and the operations of addition and scalar multiplication behave in the same way in both the subspace and the original vector space.

4. How can I determine if a set of vectors forms a subspace?

To determine if a set of vectors forms a subspace, you can check if the vectors satisfy the conditions for a vector space. This includes checking if the vectors are closed under addition and scalar multiplication, if they have a zero vector, if they are associative and commutative under addition, if they have an inverse element for addition, and if they satisfy the distributive property for scalar multiplication over addition.

5. What are some applications of linear algebra and vector spaces?

Linear algebra and vector spaces have many applications in various fields such as physics, engineering, computer science, and economics. Some examples include using vector spaces to model physical systems, using linear transformations to analyze data in machine learning, and using matrices to solve systems of equations in economics.

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