Fundamentals of Computer Science II (CSC-152 97F)
Outline of Class 52: Overview of CS
The outline of today's class is still in draft form.
- Reminder: We'll have a review session Tuesday, Dec. 16th at 7pm.
- My "laboratory" now has one Macintosh, an 8600/300. You should feel
free to use it, but don't abuse it (it's in the MathLAN laboratory).
- Today and tomorrow we'll do a short "tour of computer science".
I hope you'll find the material interesting, but you don't need to
worry about it being on the final.
- In order to implement any of the aforementioned algorithms, we'll
Graph data structure.
- How do we implement graphs (directed graphs, weighted graphs, etc.)
- As in other cases, it may depend on the way you intend to use the
graph. For example, if you frequently attempt to list all the
neighbors of a node (as we did in a few recent algorithms), you
want something that provides an answer to that question quickly.
- There are three main implementations of graphs:
- Edge lists
- Adjacency lists
- Adjacency matrices.
- In an edge list implementation, the graph is implemented
as a list of edges (pairs of nodes, potentially ordered, potentially
- This makes it easy to get a list of all the edges :-).
- This makes it easy to add edges.
- This makes it easy to present the graph in textual form
(just list the edges).
- This makes it difficult to get all the neighbors of a node,
as we must look at all the edges in the graph.
- This makes it difficult to determine whether or not a node
is in the graph.
- In an adjacency list implementation, we associate a list
of neighbors (plus edge weights if appropriate) with each node.
Often, we store the individual nodes in a dictionary for quick
- This makes it nearly as easy to add edges.
- This makes it relatively easy to get a list of all edges.
- This makes it easy to get all the neighbors of a node.
- This makes is slightly difficult to determine if there is an
edge between two nodes (it's O(#neighbors)).
- In an adjacency matrix implementation, we build a matrix
of all pairs of nodes. Each entry in the matrix reflects the weight
of the edge from one node to the corresponding node. (In a non-weighted
graph, it might simply contain a boolean value: true for edge, false
- This makes it easy to determine if there is an edge between two
- This makes it easy to add edges (O(1)).
- This makes is slightly more difficult to get lists of edges
- Can you think of other reasonable implementations?
- What is computer science? At times, it depends on who you ask.
- An engineer might tell you that it's the study of how to build
computers. Others would call this Computer Engineering.
- One of the faculty at my old institution might tell you that it's
the study of computational complexity -- what can and cannot be
computed. Others might call this a part of
Theoretical Computer Science.
- A programmer might tell you that it's the study of programming.
Others might call that Software Engineering.
- A typical computer user might guess that it's the study of
computer applications, like word processors or spreadsheets.
- A humanist might tell you that computer science is a preprofessional
specialization, unfit for a liberal arts education. Others might
call that severely biased. (Yes, that was intended as a joke.)
- While CS encompasses these ideas, it is much more.
- A book I like (Schneider and Gersting's An Invitation
to Computer Science) suggests that
Computer Science is the study of algorithms, including their
(1) formal and mathematical properties, (2) hardware realizations,
(3) linguistic realizations, and (4) applications.
- I would extend that to say that computer science is the study
of algorithms and data representation, since some of what we do
in CS focuses more on the data than on the algorithm (as we've
seen in this course).
- Computer Science lies at the intersection of a number of intellectual
- In fact, the recommended computing curriculum suggests that we approach
each topic in CS from each of these perspectives.
- If we're to do that, we need definitions. That's up to you ...
(it may be less fun in this class than in other classes I've taught).
- Like many modern fields, computer science has segmented into a number
of subfields. There is some overlap between many of these subfields,
but there are also some clear divisions.
- Some of the ones that come to mind are ...
- Computer architecture -- the design and analysis of the
primary electronic components of computers.
- Programming languages -- the design, analysis, and
implementation of the languages we use to express computation.
- Algorithms -- the formal design and analysis of procedures
for solving problems.
- Computability theory -- the study of what can and cannot
- Human-computer interaction -- the study of how computers can
be made easier to use and how computers affect human task-completion
- Information retrieval -- mechanisms for organizing data for
quick access; models and systems for access to data.
- Multimedia -- the electronic integration of text, audio, video,
graphics, animations, ....
- Operating systems -- how to provide a robust and reliable
computing system upon which other applications rely. Concerns itself
with processes (programs as they execute), memory, file systems, and
- Networking -- how computers can communicate with each other
reliably and quickly.
- Parallel and distributed computing -- the use and coordination
of multiple computers (and computer components) acting together.
- Applications -- the things we can do with computers.
- Software engineering -- the formal discipline of writing
- Artificial intelligence -- the attempt to simulate human
thought processes on computers.
- Social issues -- the effects of computers on humans and society.
- Obviously, it won't be possible to discuss all of these in any detail.
We will, however, attempt to visit an interesting problem (or problems)
in many of the subfields.
- We won't visit these subfields in any particular order.
- The goal of operating systems design is to provide a mechanism by which
programs can easily and reliably use system resources.
- These resources include the computer's memory, the hardware, and such.
- The problem becomes more difficult as you need to have your computer handle
multiple "cooperating" processes. For example, on the HP's you can
be simultaneously running a mail reader, netscape, emacs, and more.
- What happens if two processes try to modify the same file?
- What happens if the total amount of memory the processes need is
greater than the actual memory on the machine?
- Given that most processors can only run one process at a time,
how do you simulate multiple processes?
- The subfield of programming languages concerns itself with
the languages used to describe algorithms ("their linguistic
- It has its own subsubfields that include the design of languages,
the implementation of languages, and even the theoretical power of
- You've already seen a number of key issues in programming language
- Key paradigms: Imperative, Functional, and Object-oriented (yes,
there are others).
- Error-handling mechanisms (e.g., exceptions vs. "test then do").
- Translation (implementation of one thing using another).
- One of my favorite concepts in languages is that of lazy
- Consider a simple language in which you can define functions and
- One problem in the design of such a language is how to deal with
the application of a function to arguments.
- You might choose to evaluate the arguments and then pass the
results to the function.
- You might choose to pass the unevaluated arguments to the function.
In this case, you'll eventually need to evaluate arguments in order
to get some output.
- For example, given the definition
square(x) = x*x
- You could evaluate
square(2+3) by ...
- first evaluating
2+3 (getting 5), then replacing
5*5, then replacing
5*5 by 25.
then evaluating each of the
(2+3)'s, then replacing
5*5 by 25.
- The first strategy is called innermost, the second is often
- Which is better? Well, it depends on how extreme you're willing to
be. If you believe that you should only use one strategy, then
innermost can have serious disadvantages.
- Consider the factorial function, which might be defined as
fact(n) = if(n==0, 1, n*fact(n-1))
- Observe that
if() is also a function.
if might be defined as
if(true,T,F) = T
if(false,T,F) = F
- Consider the evaluation of
-> if(1==0, 1, 1*fact(1-1))
-> if(false, 1, 1*fact(1-1))
-> if(false, 1, 1*fact(0))
-> if(false, 1, 1*if(0==0, 1, 0*fact(0-1)))
-> if(false, 1, 1*if(true, 1, 0*fact(-1)))
-> if(false, 1, 1*if(true, 1, 0*if(-1==0, 1, -1*fact(-1-1))))
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