Transcript CS503: First Lecture, Fall 2008

CS305/503, Spring 2009
Introduction
Michael Barnathan
Welcome
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Who I am:
– Michael Barnathan – Monmouth University CS alumnus, now an adjunct professor.
– This is a course I originally taught at Monmouth University during Spring 2009.
– I now donate the slides to the Polymath Foundation in hopes that they will edify additional
students. The slides may periodically contain references to a class that no longer exists 
– I’ll record voiceovers, but only as I have the time. It may take a while. Please be patient.
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What this course will be about:
– Data Structures
– Algorithms
– Java
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Prereq:
Basic Java and object-oriented programming experience.
What I expect of you:
– It’s like learning to play an instrument:
– View the lectures – don’t miss your “lessons”.
– Keep programming on your own – “practice”.
– You can eventually learn how to play by practicing without taking lessons (though it will take longer),
but you’ll never learn how if you go to lessons but don’t practice!
– Much as I like teaching, this class is offered for your own benefit! Data Structures and
Algorithms form the core of CS. You will use these skills whenever you touch software design
in any context, and this is the best opportunity you will ever receive to learn them.
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Your goals:
Let them guide your learning.
Syllabus
• It’s online.
• If you have any questions about it, feel free to
email me.
Java
• This is the first time this course is being taught in Java (it was previously
taught in C++).
• Have any of you worked with Java before?
• Because Java may be new to some of you, we will cover it in some detail.
However:
– You should also make an effort to learn the language on your own (it’s fairly
similar to C++).
– You will be expected to know the language on the assignments and exams.
– You will also be expected to know how to write, compile, and execute a Java
program without using an IDE.
• The systems in this lab are unreliable when it comes to running IDEs. They may not
always work. Even when they do, you may find yourself struggling with the IDE longer
than it would have taken you to just write the code!
• I recommend learning how to use vim or emacs, as these are very powerful editors, but
failing that, at least know how to use nano (which is like Notepad for the Linux terminal).
• Know how to invoke the javac and java commands to compile and run your programs.
What is a Computer Program?
• What is a computer?
• What is a program?
• What are the key components of a program?
• Must things be this way?
– Must a computer be a machine?
– Need a program run on a computer?
– Can a program be created while avoiding those
components?
The Class’ Model
• According to the class, a computer:
– Is a machine (what’s a machine?)
– Performs tasks designated by a user.
– Has security in place to protect its integrity.
• And a program:
– Interacts with the computer/user (I/O).
– Consumes system resources.
– Accomplishes a goal.
What is a computer?
Given infinite time and space, you CAN
build a computer using rocks*.
Or neurons…
Computer systems are actually organized in
a fairly similar manner to brains.
* Warning: do not attempt in a finite universe
unless otherwise recommended by a physicist.
--xkcd
First Two Computers
Here the first known computer is
seen using the second to perform a
calculation.
--Office online clip art.
Turing Machines
• Computers can actually be implemented with abstract models that don’t
exist at all…
• One such example is a Turing machine.
• This model consists of:
– An infinite length of tape (“memory”, “storage”)
– A head, which can read or write a single strip of tape and move one strip left
or right at a time.
– A stack, containing instructions and a stack pointer.
• Why implement a fake computer?
– Because Turing machines are able to perform any calculation a real machine
can.
– Therefore, things that cannot be done on a Turing machine can’t be done on
real machines either.
• A machine or programming language capable of performing every
computation that can be performed on a Turing machine is called turing
complete.
Data Structures and Algorithms:
What are they?
• Data structures are models.
– They are means of arranging data within your program.
– Choosing the right arrangement can make your job easier!
• Algorithms are recipes.
– For solving problems in general:
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Programming.
Cooking.
Driving.
Solving math problems.
Many other areas.
– You don’t need a computer to execute them.
– In fact, very often you are the one executing them:
• Following driving directions.
• Solving puzzles.
• Interacting with a program (such as one we’ll discuss today).
What is the difference between an
algorithm and a program?
• The differences are slight.
• Programs are implementations of data structures and
algorithms.
• Data structures and algorithms are abstract concepts.
• Just as the Turing machine was an abstract computer.
• Programs are generally directly interpretable, and can
somehow be used directly to solve problems.
– Very often you will need to “translate” an algorithm, given
in pseudocode or by a diagram, into a program.
– Sometimes you’ll derive an algorithm but leave the
implementation to others.
– Often you’ll be expected to do both!
How do we measure algorithms?
• Correctness:
– Produces the correct answer given the correct input.
• “Halting”:
– Stops after it does what it’s supposed to.
– Doesn’t crash.
• Performance:
– Time (e.g. CPU time)
– Space (e.g. Memory)
• Development time – don’t overlook it.
• Is saving 5 seconds per run worth a week of your time? Unless the
app. is time-critical, probably not.
• Complexity:
– Is the solution simple, intuitive, easy to understand?
Tradeoffs
• As computer scientists, you will make many
tradeoffs:
– Space vs. time.
– Space and time vs. complexity.
– Everything vs. dev. time.
• “Optimization” of an algorithm takes time and effort.
• Making these tradeoffs wisely is what
separates “good” from “great”.
Measuring Time and Space
• An abstract notion, not usually measured in seconds, but “units”
that only have meaning relative to one another.
– For example, “2” takes twice as long as “1”, but we have no idea how
long “1” takes.
• We care only about the order of growth.
– Not how long something takes.
– Instead, how much longer it takes with more data.
• We’ll start with “Worst-case” analysis.
– Murphy’s Law: assume that everything that can slow the algorithm
down will.
– If your algorithm runs slower when it reads in the number “2”, worst-case
analysis would have you assume every element in the dataset is 2!
– However unlikely this may be – it is the worst case, not the worst probable
case.
– The purpose of this is to find out the absolute longest amount of time
/ largest amount of space that an algorithm might use.
• There’s also a “best-case”, which assumes the opposite.
Big-O (“Asymptotic”) Notation.
• O(f(n)), where f(n) is some function; e.g.:
Best
Worst
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O(1)
O(log n)
O(n)
O(n log n)
O(n^2)
O(n^p)
O(2^n)
“Constant time”
“Logarithmic”
“Linear”
“n log n” (less common: “Linearithmic”)
“Quadratic”
“Polynomial”
“Exponential”
• This only indicates the dominant term of the algorithm’s growth.
– For example, the function 3n^2 + 10n + 5 is O(n^2).
• We don’t take constant terms into account.
– y = .0001x^2 is worse than y = 10000x.
– This is because as x gets large, the x^2 term will dominate.
• Therefore, even if algorithm B takes twice as long as algorithm A, they
would still belong to the same complexity class.
– There is no such thing as “O(2n)”; it’s just O(n). Constants are not counted.
Big-O visualized
How bad is exponential time?
This bad:
O(2^n)
O(n log n)
Other Symbols
• Big-O is only one of the symbols used in asymptotic analysis.
• Others are Θ and Ω.
– O represents an upper bound (“no worse than”).
– Ω represents a lower bound (“no better than”).
– Θ represents a tight bound (“exactly; no better or worse than”).
• It is not wrong to say a linear function is O(n2); it just means we
don’t know enough to refine our estimate. O(n) represents a
“tighter” bound – and a more complete state of knowledge about
the running time of the algorithm.
– It is wrong to say the function is anything other than Θ(n), however. An
Θ(n) bound, if correctly proven, will never change.
• For the next few weeks, we will use O and Θ interchangeably (many
sources do, although it’s not precise to do so) and will avoid Ω
altogether. But do be aware of the difference; we’ll start to
distinguish between them later.
Optimality
• Unfortunately, exponential time is not always avoidable.
• Certain problems can’t be solved any faster.
• Thus, an “optimal” solution might not be fast…
– It’s just the fastest one that can exist.
• Our subject is the study of algorithms: how to solve problems
efficiently.
• The study of how quickly problems can be solved by any algorithm
is called complexity theory.
– We unfortunately won’t have time to cover it.
– There is a famous unsolved problem in this field known as “P=NP?”
– This has to do with whether a certain class of problems whose solutions can
be verified in polynomial time can also be solved in polynomial time.
– If not, these algorithms are all exponential (or worse!)
• There’s a $1 million prize for solving it, but it’s an extremely difficult question!
The Guessing Game:
Writing a simple Java program.
• Problem:
– Guess a random number from 1 to 100.
– Prompt the user to input guesses.
– Print out whether the number is actually higher,
lower, or equal to the user’s guess.
– Repeat until the user guesses correctly.
• How would you do this in Java?
– The answer is in Thursday’s notes.
Decomposition of the problem
• It helps to make problems simpler; to iteratively take
them down to the level at which you can implement
them.
• “Higher”/“Lower”/“Equal” translate into “if”
comparisons between the number and the guess.
• “Repeat until…” usually means “use a loop”.
• “Prompt the user” means you’ll be using Java’s input
facilities (hint: look up the Scanner class).
• Random numbers can be generated using
Math.random() or the Random class.
• When in doubt about how to use a class, consult the
Java API documentation!
That’s all for now!
• The lesson:
– Algorithmic thought exists outside of computer
science. You can apply what you will learn in this
class in many unexpected places.
• Next class: The guessing game, review of
asymptotic analysis, arrays.