Math for Non-Geeks/Sequence spaces

oben The sequence space is a vector space consisting of infinitely long tuples . The operations on the sequence space are component-wise addition and scalar multiplication. Motivation We have already learned about the coordinate spaces for a field as an example for vector spaces. Here, each element consists of distinct, that is, finitely many, entries from . For example, is an element of . We can also consider infinite tuples. For example, is such an "infinite tuple". A better name for infinite tuples is "sequence". If is the field of real or complex numbers, these are exactly the already known sequences from calculus. How do we define the vector space operations on the sequences? On we have defined the operations component-wise. We already know that we can also add and scale sequences component-wise. Therefore, we can also define addition and scalar multiplication on infinite sequences over arbitrary fields. This leads us to the conjecture that the set of all sequences with entries in should form a vector space. We call it the sequence space over . We will first define the sequence space precisely and then prove that it is indeed a vector space. Then, in the section Subspaces of the sequence space, we will consider examples of subspaces of the sequence spaces over the real and complex numbers, which are important for advanced calculus. Notation Let be a field. We always write instead of in this article for sequences with elements from . Definition of a sequence space The sequence space as a set We define the set We call it the set of all sequences over , or the sequence space over . In analogy to the coordinate space, we can also define an addition and a scalar multiplication on : Vector space operations on The addition is defined by Similarly, we define scalar multiplication by The sequence space is a vector space is a vector space is a -vector space. We proceed as in the article Proofs for vector spaces. Because the sequence space is defined similarly to the coordinate space, we use the same strategy as for the proof that the coordinate Space is a vector space. We have to check the eight vector space axioms. Associativity of addition Let . Then: This shows the associativity of the addition. Commutativity of addition Let . Then: This establishes the commutativity of the addition. Neutral element of addition We now have to show that there is a neutral element , that is, for all . Since we trace all properties back to the properties in , we choose as an approach for the neutral element. Let . Then: Thus, we have shown that is the neutral element of addition. Inverse with respect to addition Let . We need to show that there is a such that . As with the neutral element of addition, we use the corresponding counterpart from as a starting point. That is, we choose for the sequence . Then Thus, we have shown that for any there is a with . Scalar distributive law Let and . Then: Thus, the scalar distributive law is shown. Vector Distributive Law Let and . Then: Thus, the vectorial distributive law is shown. Associativity with respect to multiplication Let and . Then: This establishes the associative law for multiplication. Existence of a unit Let . Then: Thus, we have shown the unitary law. Thus, we have established all eight vector space axioms and is a vector space. Subspaces of the sequence space The sequence space has some frequently used subspaces. Most of these subspaces can be defined only over the fields and . They have many applications in functional analysis, where they are part of an important class of examples. In the field of linear algebra over arbitrary fields, the space of sequences with finite support serves as an example in many places. It is the simplest example of an infinite-dimensional vector space and thus can be used as a good example where statements cease to hold, as the vector space if "too large". The subspace of sequences with finite support Folgen mit endlichem Träger Set of sequences with finite support We define . The sequences with finite support form a subspace is a subspace. We check the three subspace criteria. The zero element of is the sequence which is constant . Thus, for , for all , it holds that , viz. . is closed under addition. Let . By assumption, there exist with the property that for all and for all . We set . Then, for all we have that . This shows . is closed under scalar multiplication. Let and . By assumption, there exists with the property that for all . Then for all , it holds that . Hence . Thus, we have shown that is a subspace of . For example, the notation for the space of sequences with finite support can be derived like this: This vector space is a subspace of the space of zero sequences over the fields or . The latter subspace is usually denoted by . The stands for convergence, and the for the fact that we put only zero sequences of the convergent sequences into the vector space. When talking about convergence, the condition that the sequence eventually becomes is of course significantly stronger than the condition to converge against . Therefore, the space of sequences with finite support gets an additional zero into the index. Subspaces from calculus In the following, we assume that . UnterraumBeschrFolgen Vector space of bounded sequences The subset is a subspace of the sequence space. The space is called vector space of bounded sequences. We check the three subspace criteria. The zero element of is the sequence which is constant . This is of course bounded (e.g. by ) and thus lies in . is closed under addition Let . Then . From the triangle inequality for all we obtain Thus . is closed under scalar multiplication. Let and . Then . Since for all we obtain Hence . Thus, we have shown that is a subspace of . Vector space of convergent sequences The subset is a subspace of . It is called vector space of convergent sequences. We check the three subspace criteria. The zero element of is the sequence which is constant . This obviously converges to and thus lies in . is closed under addition Let . Then, . We have . The two limits on the right-hand side exist by assumption, so the limit on the left-hand side exists. In particular, converges, so it is true that . is closed under scalar multiplication. Let and . Then, . We have . The two limits on the right-hand side exist by assumption, so the limit on the left-hand side also exists. Hence . Thus, we have shown that is a subspace of . Vector space of zero sequences The subset is a subspace of . This subspace is called vector space of zero sequences. We check the three subspace criteria. The zero element of is the sequence which is constant . This naturally converges to and thus lies in . is closed under addition Let . Then . We have . Consequently, converges to . Thus . is closed under scalar multiplication Let and . Then . We have . Hence . Thus, we have shown that the space of zero sequences is a subspace of . Vector space of absolutely summable sequences The subset is a subspace of . It is called the vector space of absolutely summable sequences. We check the three subspace criteria. The zero element of is the sequence which is constant . For this sequence holds. So . is closed under addition Let . Then . Because of the triangle inequality for all we obtain . With the sum rule for series we get . Hence . is closed under scalar multiplication Let and . Then . Since for all it follows that . Hence . Thus, we have shown that is a subspace of . Relationships between the subspaces We have now learned about some subspaces of the sequence space for . This raises the question of what relations exist between them. Most of the conditions we used to construct the subspaces are conditions from calculus. Fortunately, there are already results in calculus that describe implications between the individual conditions. If we translate these implications into the world of sets and vector spaces, we get the following result: Inclusions between the subspaces It is true that . We will now show the four inclusions one after the other. Thereby we also prove that in each case no equality is valid. Let . By definition there exists some , such that for all . Thus and we obtain . This shows . Consider the sequence . We have , since . But , since for all . This shows . Let . This means, . Then, by means of the term test, we have that . That is, and thus . This shows . Consider the sequence . We have , since . But , since diverges by means of the theorem on the divergence of the harmonic series. This shows . This is clear since every sequence converging to zero converges. Consider the sequence . We have , since . But , since . This shows . From calculus we know, that every convergent sequence is bounded. So the inclusion holds true. Consider the sequence . We have , since for all . But , since does not exist. This shows . We already know this because is a subspace of . Consider the sequence . We have , since . This shows . unten