Linear Algebra: Linear Combinations, Span, Linear Independence
Linear Algebra: Linear Combination
Linear combinations of vectors are sums of a set of vectors multilpied by constants. For example, if we had and , a linear combination of them could be .
Linear Algebra: Linear Independence
We can imagine two vectors that point in the same direction, that their is a constant that when multiplied by one of the vectors gets us the other vector. is an example of linearly dependent vectors. They lie on the same line. Linearly independent vectors require more than just a scaling, and therefore define two lines. The test for linear independence/dependence is applicable in any number of dimensions: where at least one is non-zero means the vectors are linearly dependant.
Any non-zero constant can satisfy this equation , so we know are linearly dependent.
0’s are the only values that satisfy these equations, so we know are linearly independent
Linear Algebra: Span and Basis
defines all the linear combinations of , and is the ”span” of and . A Span will either define a single line if the vectors are linearly dependent or a vector space if all vectors are linearly independent. are linearly independent. If they are linearly independent, then the vectors form a basis of , meaning the linear combinations of describe EVERY vector in .