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CSCI 311 – Algortihms and Data Structures

CSCI 311 – Algortihms and Data Structures. http://www.eg.bucknell.edu/~csci311 Steve Guattery Dana 335a (department office) x7-3828 guattery@bucknell.edu http://www.eg.bucknell.edu/~guattery.

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CSCI 311 – Algortihms and Data Structures

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  1. CSCI 311 – Algortihms and Data Structures http://www.eg.bucknell.edu/~csci311 Steve Guattery Dana 335a (department office) x7-3828 guattery@bucknell.edu http://www.eg.bucknell.edu/~guattery

  2. This course does not have a lab, but it does have a recitation that meets Thursday afternoons. We will meet tomorrow. Assignment: Read Appendix A and Section 3.2 in the text.

  3. What is a data structure? “A data structure consists of a base storage method (e.g., an array) and one or more algorithms used to access or modify that data.” “A data structure is how information is stored in the computer; a data type is a data structure plus its operations.” Key point: Data structures determine how data is stored on a computer. They are part of an implementation.

  4. What is an algorithm? “A sequence of computational steps that transform the input to the output.” “A problem-solving method suitable for implementation as a computer program.” Key points: Algorithms are not (necessarily) implementations. They are often described in terms of mathematical objects (e.g., ordered array, tree, graph) that may or may not immediately correspond to a data structure.

  5. What is an abstract data type (ADT)? An ADT specifies the data and operations that will be used in a data type, but does not specify the implementation. ADTs serve as a bridge from an algorithmic description to an implementation using data structures.

  6. Process for creating a program: Real-world problem on real-world objects ↓ Mathematical representation of problem and objects + algorithm(s) ↓ Computer implementation on data structures

  7. The Big Question for This Course: Resource usage of algorithms and data structures. What resources do they use?

  8. Resources for algorithms and data structures: • Time (instructions/machine cycles) • Space (memory) • We will focus mainly on time.

  9. We want algorithmic comparisons to be: • independent of processor (abstract step) • independent of implementation (ignore start-up overhead and constants) • related to how the resource depends on problem size (asymptotic analysis)

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