We can use vector spaces for thinking about things that aren’t primarily concerned with physical space like we are in Blender. Let’s imagine something practical, if a bit absurd. Pretend we have unlimited access to three kinds of dough. Each has flour, water, and yeast in different ratios. What we don’t have is access to the individual ingredients.
Suppose we want a fourth kind of dough which is a different ratio of the ingredients from the doughs we have. If the ratios of the ingredients of the three doughs we already have are unique, then we are in luck! We can make that dough we want by combining some amount of the three we have. In fact, we can make any kind of dough that is a combination of those three ingredients. In linear algebra, this is called linear independence.
Each dough is a vector, and each ingredient is a component. We have three equations (doughs) in three variables (ingredients).
This is a three dimensional vector space, which is easy to visualize. But there is no limit to how many dimensions you can have, or what they can represent. Some economic models use vectors with thousands of dimensions representing inputs and outputs of resources. Hopefully my explanation helps us see how vectors can sometimes be more difficult to imagine as directions and magnitudes.
I’ll give it a shot.
We can use vector spaces for thinking about things that aren’t primarily concerned with physical space like we are in Blender. Let’s imagine something practical, if a bit absurd. Pretend we have unlimited access to three kinds of dough. Each has flour, water, and yeast in different ratios. What we don’t have is access to the individual ingredients.
Suppose we want a fourth kind of dough which is a different ratio of the ingredients from the doughs we have. If the ratios of the ingredients of the three doughs we already have are unique, then we are in luck! We can make that dough we want by combining some amount of the three we have. In fact, we can make any kind of dough that is a combination of those three ingredients. In linear algebra, this is called linear independence.
Each dough is a vector, and each ingredient is a component. We have three equations (doughs) in three variables (ingredients).
This is a three dimensional vector space, which is easy to visualize. But there is no limit to how many dimensions you can have, or what they can represent. Some economic models use vectors with thousands of dimensions representing inputs and outputs of resources. Hopefully my explanation helps us see how vectors can sometimes be more difficult to imagine as directions and magnitudes.