COP 3530: Computer Science III

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COP 3530 Computer Science III Summer 2005
Introduction
Instructor Dr. Mark Llewellyn
markl_at_cs.ucf.edu CSB 242, [phone] Cours
e Webpage http//www.cs.ucf.edu/courses/cop3530/
sum2005
School of Computer Science University of Central
Florida
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What You Should Already Know

• Asymptotic analysis
• Big-Oh (upper bound)
• Big Omega (lower bound)
• Big Theta (asymptotically equal)
• Be able to program in Java.
• Randomization and Probability Distributions.
• Basic data structures and their implementations
• Stacks
• Queues
• Lists
• Trees
• Graphs

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What You Will Learn in CSIII

• You will be exposed to a wide variety of advanced
  data structures that are based upon the classic
  data structures with which you are already
  familiar.
• Advanced list structures.
• Skip lists, self-organizing lists, etc.
• Advanced tree structures.
• 2-4 trees, red-black trees, splay trees, B-trees,
  etc.
• Advanced graph applications and algorithms.
• Network flow analysis, circuits, paths, etc.
• You will deal with the design and analysis of
  algorithms in a more detailed manner than you
  experienced in CSII.
• You will be introduced to parallel execution and
  parallel algorithms.

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Syllabus

• Link to course webpage and syllabus
• http//www.cs.ucf.edu/courses/cop3530/sum2005

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What is an Algorithm?

• What is an algorithm?
• A step-by-step procedure for performing some task
  in a finite amount of time.
• Input algorithms typically receive input.
• Output algorithms typically produce output.
• Precision the steps are precisely stated.
• Determinism the results of each step are unique
  and determined only by the input and the results
  of previous steps.
• Finiteness the algorithm terminates after a
  finite number of steps.
• Correctness the algorithm produces the correct
  results.
• Generality algorithms typically apply to a set
  of possible inputs.

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What is a Good Algorithm?

• How do we determine what constitutes a good
  algorithm?
• Time how long does it take the algorithm to
  solve the problem?
• Space how much memory is required by the
  algorithm to solve the problem?
• How are algorithms and data structures related?
• What other factors influence algorithm design and
  execution?
• Problem size
• Computing machine
• Quality of the implementation
• Others

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Methodologies for Analyzing Algorithms

• How do we analyze an algorithms performance?
• Experimentally if the algorithm has been
  implemented, just execute it and see how long it
  takes or how much memory it requires.
• Are the test data sets representative of the
  actual data?
• Difficult to compare the efficiency of two
  algorithms?
• Requires implementing the algorithm.
• Analytically a function f(n) is associated with
  each algorithm that characterizes the running
  time of the algorithm in terms of the input size
  n.
• does not require implementation of the algorithm.
• allows easy comparison of algorithms independent
  of hardware and software environments.
• is independent of the input.

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Next Class

• Read Chapters 1 and 2 in the textbook.
• Next topic