90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 1 Lecture 1: Introduction Data Structures and Algorithms for Information Processing Lecture 1: Introduction

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 2 Lecture 1: Introduction Course Web Site

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 3 Lecture 1: Introduction Structure of the Course Lectures / class participation Homeworks (programming) Midterm examination Final examination

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 4 Lecture 1: Introduction Readings Readings from the required text are assigned for each lecture -- read them in advance The book contains self-test exercises in each section Work these exercises (especially if you are new to the material)

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 5 Lecture 1: Introduction Grading Homework(5-6)50% Midterm Exam25% Final Exam25%

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 6 Lecture 1: Introduction Definitions A data structure is an organized collection of data with specific aggregate properties An algorithm is a sequence of program instructions designed to compute a particular result

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 7 Lecture 1: Introduction Five Steps per Datatype Understand it Abstractly Write a Specification Write Applications Select an existing, or Design and Implement our own Analyze the Implementation in terms of performance

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 8 Lecture 1: Introduction Abstract vs. Concrete An abstract data type (ADT) specifies behavior, but not implementation An implementation uses a particular low-level datatype to implement the desired behavior

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. Quick Example A stack is an abstract data type (ADT). We push and pop from the top. Consider three implementations: (1) Every push causes all elements in an array to shift down 1 before the insertion at s[0]. (2) We add at stackTop and then add one to stackTop. (3) A linked list use where the top is always at the list’s front end. Each implementation can be written correctly. Implementation (1) runs in Θ(n). Implementations (2) and (3) run in Θ(1). 9 Lecture 1: Introduction

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 10 Lecture 1: Introduction Step 1: Understanding Grasp the datatype at the level of concepts and pictures e.g., visualize a stack and the operations of pushing / popping Understand simple applications Simulate by hand e.g., use a stack to reverse the order of letters in a word

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 11 Lecture 1: Introduction Step 2: Specification Write a specification for a Java class that could implement the datatype (see javadoc Appendix H) Headings for constructor, public methods, public features Includes precondition / postcondition for each method Independent of implementation!

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 12 Lecture 1: Introduction Step 3: Application Based on the specification, write small applications to illustrate the use of the datatype “Test the specification” prior to implementation Code not yet compiled / run

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 13 Lecture 1: Introduction Step 4: Implementation Select appropriate data structure Implement as private class vars Write rules relating instance variables to abstract specification: the invariant of the ADT Each method knows the invariant is true when it starts Each method upholds the invariant

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 14 Lecture 1: Introduction Step 5: Analysis Correctness Flexibility When possible, compare different implementations of the same ADT Time Analysis –number of operations –big-O notation, e.g.,

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 15 Lecture 1: Introduction Phases of Software Development Specification of the Task Design of a Solution Implementation of the Solution Running Time Analysis Testing and Debugging Maintenance and Evolution Obsolescence

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 16 Lecture 1: Introduction Java Conceived in 1991 at Sun Has Similarities to C++ Is simpler than C++ Object-Oriented Programming (OOP) –information hiding –component re-use –programs (methods) and data are grouped together into classes –we will not be using the collection classes

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 17 Lecture 1: Introduction What You Should Know Already How to use a Java development environment (java, javac, javadoc) How to write, compile, and run short Java programs Java primitive types (number types, char, boolean) and arrays Easy to pick up with prior programming experience

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 18 Lecture 1: Introduction Specifying a Java Method Specification: what a method does not how it does it A form of information hiding called procedural abstraction Method signature public static double celsiusToFahrenheit(double c) Method call double fahrenheit = celsiusToFahrenheit(celsius);

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 19 Lecture 1: Introduction Elements of Specification Short Introduction Parameter Description Returns –Specify the meaning of return value Throws list –error conditions (exceptions) “thrown” by this method Precondition and Postcondition

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 20 Lecture 1: Introduction Preconditions A precondition is a statement giving the condition that should be true when the method is called The method is not guaranteed to perform as it should unless the precondition is true. If violated, ignore (like c) or throw an exception (like Java).

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 21 Lecture 1: Introduction Postconditions A postcondition is a statement describing what will be true when a method call completes If the method is correct and precondition was met, then the method will complete, and the postcondition will be true when it does Use Java assert statement for post- condition checks during debugging. Not during deployment.

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 22 Lecture 1: Introduction Example Specification celsiusToFahrenheit public static double celsiusToFahrenheit(double c) –convert a temperature from Celsius degrees to Fahrenheit degrees –Parameters: c - a temperature in Celsius degrees –Precondition: c>= –Returns: temperature c in Fahrenheit –Throws: IllegalArgument Exception

90-723: Data Structures and Algorithms for Information Processing Copyright © 1999, Carnegie Mellon. All Rights Reserved. 23 Lecture 1: Introduction More on Pre/Postconditions Slides from Main’s LecturesSlides from Main’s Lectures