Introduction and a Review of Basic Concepts Syllabus and goals of the course Object oriented programming contrasting to C programming Objects, classes, methods Instantiation, references Polymorphism, inheritance Constructor, destructor, garbage collection Encapsulation Public, private, protected Data structures already introduced: Stacks, queues, linked lists Recursion and induction Understanding data structures and algorithms is a major difference separating computer scientists from one who reads a manual and begins to program.

C versus Java The dot in Java is equivalent to the -> C; there is no Java equivalent to the C dot operator. Generally, use -> in most cases and declare variables using the ‘*’ (indicating pointer) There is no Boolean in C. Many programmers declare TRUE and FALSE using #define C has no garbage collection. To avoid memory leaks, make sure to free whatever you malloc. Use NULL in C, instead of the null in Java Use scanf for input, but make sure to clear the input buffer after an error (scanf("%s*")) C has no methods (it has functions), so dot notation makes no sense. Replace: object.method by function(data There is no length property in arrays and strings. Array sizes must be stored in a separate variable. Be careful with pointers and memory management. C doesn’t catch all errors at compile time.

Strings in C There is no String type, use an array of characters (char *data). C has no “+” operator for string concatenation. Use strcat, but make sure you have an allocated area in memory to hold the larger string. To truncate a string, store '\0' in the appropriate index. To format output, use printf. For both puts and printf, make sure to follow the output with fflush(stdout);. Otherwise, output only occurs when the accumulated print buffer gets large enough. There are many string functions in C, all starting with str. There is also memory manipulation functions, all starting with mem. Make sure your strings end with a null. Java has the length in the front of a string; C has a null at the end.

Analysis of Algorithms Definition of Algorithm. Why study data types and algorithm? Step-by-step solution to a problem Often the problem can be specified in terms of the size of the data set (n). Algorithm performance (The technical term is complexity) A good algorithm implemented by a bad programmer will beat a bad algorithm implemented by a good programmer A time function, f, of the dataset size is often used to measure effectiveness as the data set size grows. For example, f(n) <= c*n, where c is a constant > 0. Other examples of the growth rate are c*n2, c*lg n, c, lg2 n, etc. Big Oh notation. How to determine if an algorithm is a good one? How fast does it work on a given data set? What is the Relationship between time and data set size? How does it perform on different data set sizes? Growth is more important than a given run’s performance

Big Oh Notation Measurement of algorithm efficiency in terms of memory or time as the problem size increases Formal Definition: f(n) = O(g(n)) means there are positive constants c and z, such that 0 ≤ f(n) ≤ cg(n) for all n ≥ z. The values of c and z must be fixed for the function f and must not depend on n. Explanation of terms: g(n) is a well known function to which we want to compare our algorithm. f(n) is how our algorithm performs on different size data sets z is an integer. For all data set sizes smaller than z, we don’t consider the algorithm’s efficiency c is a positive constant . By multiplying g by this constant, all functions with equal or smaller slopes will satisfy the Big Oh definition requirements. Review of logarithms. Conversion of logarithms Logn x = logq x / logq n

Pictorial View of Big Oh Time or memory z Data set size

Allocate Unique Random Numbers Declare the free list int freeList[MAX]; Initialize the free list for (i=0; i<MAX; i++) freeList[i] = i; freeCount = MAX; Allocate an entry from the free list Find a random index into the free list: index = (int)(rand() * freeCount); Get the value from the free list (key = freeList[index];) Replace the index with the last value in the freeList freeList[index] = freeList[--freeCount]; Release an entry back to the free list Put the value to the end of the free list freeList[freeCount++] = key;

Abstract Data Type A general-purpose data structure encapsulated from the user through a well-defined set of operation Design Design the operations that are to be used Design the data structure to be used Examples (What operations? What data-structure?) Java’s primitive floating point number Stacks, queues, and linked lists. To be covered: Trees, priority queues, hash tables, graphs. Arrays (Ordered and Unordered) Array operations and complexities (insert, delete, find, expand,sort,access,traverse) Advantages and disadvantages