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Lecture14

# Lecture14 - Computational Optimization ISE 407 Lecture 14...

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Computational Optimization ISE 407 Lecture 14 Dr. Ted Ralphs

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ISE 407 Lecture 14 1 References for Today’s Lecture References CLRS Section 11.1, Chapter 12 D.E. Knuth, The Art of Computer Programming, Volume 3: Sorting and Searching (Third Edition), 1998. R. Sedgewick, Algorithms in C++ (Third Edition), 1998.
ISE 407 Lecture 14 2 Hash Tables We now consider data structure for storing a dictionary that support only the operations insert , delete , and search . Most data structures for storing dictionaries depend on using comparison and exchange to order the items. This limits the efficiency of certain operations. A hash table is a generalization of an array that takes advantage of our ability to access an arbitrary array element in constant time. Using hashing, we determine where to store an item in the table (and how to find it later) without using comparison. This allows us to perform all the basic operations extremely efficiently.

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ISE 407 Lecture 14 3 Addressing using Hashing Recall the array-based implementation of a dictionary from earlier. In this implementation, we allocated one memory location for each possible key. How can we extend this method to the case where the set U of possible keys is extremely large? Answer : Use hashing . A hash function is a function h : U 0 , . . . , M - 1 that takes a key and converts it into an array index (called the hash value ). Once we have a hash function, we can use the very efficient array-based implementation framework to store items in the table. Note that this implementation no longer allows sorting of the items. Questions : What hash function should we use? What do we do if two items result in the same hash value (a collision )?
ISE 407 Lecture 14 4 Choosing a Hash Function What makes a good hash function? A good hash function minimizes collisions and is easy to compute . For a “random” key, we would like the probability of each hash value to be “equally likely.” This assures that the items are distributed evenly throughout the hash table. This is not as easy to accomplish as it sounds! It depends on what the distribution of possible key values is. We may not know the distribution ahead of time.

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ISE 407 Lecture 14 5 Significant Bits Two obvious hash functions are to simply consider either the first (most significant) or last (least significant) k bits of the key. How do we compute this hash function? Assume x is a w -bit integer. The index formed from the first k bits of x is the result of dividing by 2 w - k and rounding off, i.e., h ( x ) = b x/ 2 w - k c . The index formed from the last k bits of x is the remainder after dividing by 2 k , i.e., h ( x ) = x mod 2 k . Note that both of these hash functions must be used with a table of size 2 k .
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