CEG 221 Introduction

1. CEG 221Introduction Mr. David Lippa

2. Overview • Introduction • What’s this course about? A synthesis of language, theory, and application • Language: • Programming Languages • Modular C programming • Theory: Algorithm development & analysis • Application: Algorithm implementation • Syllabus • Review-ish • Questions

3. Programming Languages:High-Level vs. Low-Level • High-Level Languages • Close to English, but slightly slower • Easy to use and easily read/interpreted code • Platform independent • Low-Level Languages • Difficult to use but very fast • Typically is machine-specific

4. The Language: Why C? • Strengths: • Powerful, Fast, Portable yet relatively high-level language • Provides organizational structure for creation, testing, and verification of code • Provides similar design paradigms to other engineering languages, such as FORTRAN • Weaknesses: • Inferior to Object Oriented Programming (C++, Java) for releasable applications that require GUIs or greater complexity

5. Theory: Algorithms • Algorithm – a series of steps that results in the performance of a task • Common algorithms used in CS: sorting, searching, populating data structures • Algorithms are frequently analyzed based on the number of steps to complete the task based on the amount of elements to process. • Basically, answer the question: How much work does it take to process n elements in terms of n? (Big-Oh, Little-Oh notation – not covered in this course)

6. Algorithms: An Example • Assume a room full of people, each person is a processor. In practice, sorting that room full of people by height is faster than by first name. Common sense tells us this, right? • Everyone is an independent processor and can figure out where he/she belongs. Takes at most n memory accesses (look around the room at n people) to sort by height with n processors = n2 steps of work. • Sorting by name requires each person to ask every other person to figure out where he/she belongs. Takes n requests to each person (ask each person his/her last name) by n processors = n2 steps of work. • They’re the same!?

7. Algorithm Example (Continued) • No – they’re not the same • Each person asking every other person takes longer than each person scanning their eyes around the room to find their place. • One can either ask or listen, so it requires each processor to wait for its turn to speak • Everyone can look at everyone else without any such conflicts • THUS, sorting by height is the same • Both take n2 work under analysis, but when implemented, come out differently • This is part of Algorithm Analysis

8. Algorithms: Analysis & Implementation • There are various ways to implement “sorting people in a classroom.” • Divide and conquer – Selection Sort • Divide and merge – MergeSort • Move each to the proper spot – BubbleSort • Each have best/worst cases. What you also need to look at is the average case. • Pick the best depending on Best, Worst, Average Cases and the situation

9. Syllabus

10. Next Time:Review of Required Concepts • Basic data types • Normal basic types, size_t, time_t • Basic programming structure • Flow control / Repetition • Scope • Documentation of code • Modular programming • Libraries • Pass by reference (in C, by pointer) vs. pass by value • Basic algorithm development • Design document • Pseudo code

11. QUESTIONS? • My policy on questions: • You have the right to ask questions at ANY time in class. • The only stupid question is the one you don’t ask. • Any questions?