Understanding the Relationship Between the Span of Matrix A Columns and Linear Independence in Math

If the columns of A span R nm then the columns are linearly independent.

To understand the relationship between the span of the columns of matrix A and linear independence, we need to first define what it means for a set of vectors to be linearly independent

To understand the relationship between the span of the columns of matrix A and linear independence, we need to first define what it means for a set of vectors to be linearly independent.

A set of vectors {v₁, v₂, …, vₙ} is said to be linearly independent if for any scalars c₁, c₂, …, cₙ such that c₁v₁ + c₂v₂ + … + cₙvₙ = 0, the only solution is c₁ = c₂ = … = cₙ = 0.

Now, let’s consider the columns of matrix A. If the columns of A span Rⁿ (n-dimensional space), it means that any vector in Rⁿ can be expressed as a linear combination of the columns of A.

Suppose A has m columns. Then, for any vector x in Rⁿ, we can write:

x = c₁a₁ + c₂a₂ + … + cₘaₘ,

where a₁, a₂, …, aₘ are the columns of A, and c₁, c₂, …, cₘ are scalars. This implies that the vector equation Ax = 0 (where 0 represents the zero vector) has a nontrivial solution, since we can choose c₁ = c₂ = … = cₘ = 0 to satisfy it. Consequently, the columns of A are linearly dependent.

In summary, if the columns of A span Rⁿ, then the columns are linearly dependent, and not linearly independent.

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Proving the Relationship Between the Trivial Solution and Row Equivalence to the Identity Matrix

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