CSCI 311 – Algorithms and Data Structures

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CSCI 311 – Algorithms 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 Ben Razet Dana 318 x7-3397 benoit.razet@bucknell.edu http://www.eg.bucknell.edu/~bhcr001

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.

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.

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.

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.

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

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

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

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)