Linear Independance: Show {v1,v2,...,vn} is Basis of V

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In summary, a minimal spanning set {v1,v2,...,vn} for a vector space V is a set of vectors that cannot be spanned by fewer than n vectors. If this set is also linearly independent, then it is a basis for V. This is because if any vector is not linearly dependent, it can be removed without affecting the span, contradicting the definition of a minimal spanning set.
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stunner5000pt
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Suppose that {v1,v2,...,vn} is a minimal spanning set for a vector space V. That is V = span {v1,v2,...,vn} and V cannot be spanned by fewer than n vectors. Show that {v1,v2,...,vn} is a basis of V

to be a basis for V then V = span (v1,v2,...,vn}
we already have that
suppose one of thise vectors was not linearly dependant then the number of vectors in the span is less than n. But V = span of n vectors
so the set of vectors must be lienarly independant
 
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Surely the definition of a basis is that it is a minimal spanning set?

Or are you starting from linearly independent spanning set and showing any such thing is minimal, and vice versa?
 
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stunner5000pt said:
Suppose that {v1,v2,...,vn} is a minimal spanning set for a vector space V. That is V = span {v1,v2,...,vn} and V cannot be spanned by fewer than n vectors. Show that {v1,v2,...,vn} is a basis of V

to be a basis for V then V = span (v1,v2,...,vn}
we already have that
suppose one of thise vectors was not linearly dependant then the number of vectors in the span is less than n. But V = span of n vectors
so the set of vectors must be lienarly independant
Yes, to be minimal, they have to be independent. Suppose not, then you could do away with at least one (how?) and still span V (you should work out an example here), which contradicts "minimal."

See http://en.wikipedia.org/wiki/Vector_basis
 
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1. What is linear independence?

Linear independence is a property of a set of vectors in a vector space, where no vector can be written as a linear combination of the other vectors in the set. In other words, the only way to create the zero vector using the set of vectors is by setting all of the coefficients to zero.

2. How is linear independence determined?

Linear independence is determined by checking if the only solution to the equation a1v1 + a2v2 + ... + anvn = 0 is when all of the coefficients a1, a2, ..., an are equal to zero. This can be done by using Gaussian elimination or by checking if the determinant of the matrix formed by the vectors is equal to zero.

3. What is a basis of a vector space?

A basis of a vector space is a set of linearly independent vectors that span the entire vector space. This means that any vector in the vector space can be written as a unique linear combination of the basis vectors. A basis is essentially a "building block" for creating any vector in the vector space.

4. How do you prove that a set of vectors is a basis of a vector space?

To prove that a set of vectors {v1, v2, ..., vn} is a basis of a vector space V, you need to show two things: first, that the set is linearly independent, meaning that no vector in the set can be written as a linear combination of the others; and second, that the set spans V, meaning that any vector in V can be written as a linear combination of the basis vectors. This can be done by solving the equation a1v1 + a2v2 + ... + anvn = v, where v is an arbitrary vector in V, and showing that it has a unique solution.

5. Why is it important to show that a set of vectors is a basis of a vector space?

Showing that a set of vectors is a basis of a vector space is important because it allows us to easily represent and manipulate vectors in the vector space. By knowing that the basis vectors are linearly independent and span the entire vector space, we can use them as a reference point to create any vector in the vector space. This is especially useful in applications such as linear algebra, where vectors and vector spaces are used extensively.

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