%0 Book %1 SICP96 %A Abelson, Harold %A Sussman, Gerald J. %C Cambridge, MA %D 1996 %I MIT Press %K KalDesignResearch ch2 design ijtme2006 mythesis software streams %T Structure and Interpretation of Computer Programs %U http://mitpress.mit.edu/sicp/full-text/book/book.html %7 2nd

@book{SICP96, added-at = {2006-03-07T17:56:12.000+0100}, address = {Cambridge, MA}, author = {Abelson, Harold and Sussman, Gerald J.}, biburl = {https://www.bibsonomy.org/bibtex/269b149bbf1413472641b4ad76c531687/yish}, citeulike-article-id = {378177}, comment = {with Julie Sussman Section 2.2.1: Lists One of the useful structures we can build with pairs is a sequence -- an ordered collection of data objects. There are, of course, many ways to represent sequences in terms of pairs. One particularly straightforward representation is illustrated in figure 2.4, where the sequence 1, 2, 3, 4 is represented as a chain of pairs. The car of each pair is the corresponding item in the chain, and the cdr of the pair is the next pair in the chain. The cdr of the final pair signals the end of the sequence by pointing to a distinguished value that is not a pair, represented in box-and-pointer diagrams as a diagonal line and in programs as the value of the variable nil. The entire sequence is constructed by nested cons operations: (cons 1 (cons 2 (cons 3 (cons 4 nil)))) Such a sequence of pairs, formed by nested conses, is called a list, and Scheme provides a primitive called list to help in constructing lists.8 The above sequence could be produced by (list 1 2 3 4). In general, (list <a1> <a2> ... <an>) is equivalent to (cons <a1> (cons <a2> (cons ... (cons <an> nil) ...))) Section 3.5: Streams If time is measured in discrete steps, then we can model a time function as a (possibly infinite) sequence. In this section, we will see how to model change in terms of sequences that represent the time histories of the systems being modeled. To accomplish this, we introduce new data structures called streams. From an abstract point of view, a stream is simply a sequence. However, we will find that the straightforward implementation of streams as lists (as in section 2.2.1) doesn't fully reveal the power of stream processing. As an alternative, we introduce the technique of delayed evaluation, which enables us to represent very large (even infinite) sequences as streams.}, edition = {2nd}, interhash = {52fa1a6a1fcd609c851a1a4573a84fd8}, intrahash = {69b149bbf1413472641b4ad76c531687}, keywords = {KalDesignResearch ch2 design ijtme2006 mythesis software streams}, priority = {2}, publisher = {MIT Press}, timestamp = {2008-05-27T23:26:16.000+0200}, title = {Structure and Interpretation of Computer Programs}, url = {http://mitpress.mit.edu/sicp/full-text/book/book.html}, year = 1996 }