Recall that if is subset of vectors in , then is the set of all linear combinations of vectors in . In EV2 (Section 2.2), we learned how to decide whether was equal to all of or something strictly smaller.
Note the similarities between a planar subspace spanned by two non-colinear vectors in , and the Euclidean plane . While they are not the same thing (and shouldn’t be referred to interchangably), algebraists call such similar spaces isomorphic; we’ll learn what this means more carefully in a later chapter.
A planar subset of compared with the plane .
Figure11.A planar subset of compared with the plane .
Basically, you cannot prove something is true by assuming it’s true, and it’s not helpful to prove to someone that zero equals itself (they probably already know that).
Recall that in Activity 2.2.1 we used the words vector, linear combination, and span to make an analogy with recipes, ingredients, and meals. In this analogy, a recipe was defined to be a list of amounts of each ingredient to build a particular meal.
by moving everything over to the left hand side. Since we are assumming that and , it follows that , which is true, which proves that vector addition is closed.
A square matrix is symmetric if, for each index , the entries . That is, the matrix is itself when reflected over the diagonal from upper left to lower right. Prove that the set of symmetric matrices is a subspace of .
The space of all real-valued function of one real variable is a vector space. First, define and for this vector space. Check that you have closure (both kinds!) and show what the zero vector is under your chosen addition. Decide if each of the following is a subspace. If so, prove it. If not, provide the counterexample.
Let be a vector space and a subset of . Show that the span of is a subspace. Is it possible that there is a subset of containing fewer vectors than , but whose span contains all of the vectors in the span of ?