lecture5.ppt - Introduction to Algorithms Lecture 5 Recap • Show that(nlogn is the best possible running time for a sorting algorithm • Design an

# lecture5.ppt - Introduction to Algorithms Lecture 5 Recap...

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Introduction to Algorithms Lecture 5 2 Recap Show that ( n log n ) is the best possible running time for a sorting algorithm. Design an algorithm that sorts in linear time. Order statistics 3 Today’s topics Direct-accessible table Hash tables Hash functions Universal hashing Perfect Hashing Open addressing 4 Data Structures Role of data structures: Encapsulate data Support certain operations (e.g., INSERT, DELETE, SEARCH) Our focus: efficiency of the operations Algorithms vs. data structures 5 Symbol-table problem Symbol table T holding n records : How should the data structure T be organized? record key [ x ] x Other fields containing satellite data Operations on T: INSERT ( T,x ) DELETE ( T,x ) SEARCH ( T,k ) 6 Direct-accessible table IDEA: Suppose that the set of keys is K {0, 1, …, m –1} , and keys are distinct. Set up an array T [0 . . m –1] : Then, operations take (1) time. Problem: The range of keys can be large: 64 -bit numbers (which represent 18,446,744,073,709,551,616 different keys), character strings (even larger!). NIL x k T ] [ if k K and keys [ x ] = k otherwise. 7 Hash functions Solution: Use a hash function h to map the universe U of all keys into {0, 1, …, m –1} : When a record to be inserted maps to an already occupied slot in T , a collision occurs. T 0 h ( k 1 ) h ( k 4 ) h ( k 2 ) = h ( k 5 ) h ( k 3 ) m -1 k 1 k 5 k 4 k 2 k 3 K 8 Resolving collisions by chaining Records in the same slot are linked into a list. T 49 86 52 i h (49) = h (86) = h (52) = i 9 Analysis of chaining We make the assumption of simple uniform hashing: Each key k K of keys is equally likely to be hashed to any slot of table T , independent of where other keys are hashed. Let n be the number of keys in the table, and let m be the number of slots. Define the load factor of T to be = n / m = average number of keys per slot. 10 Search cost Expected time to search for a record with a given key = (1 + ) . Expected search time = (1) if = O (1) , or equivalently, if n = O ( m ) . apply hash function and access slot search the list 11 Choosing a hash function The assumption of simple uniform hashing is hard to guarantee, but several common techniques tend to work well in practice as long as their deficiencies can be avoided. Desirata: A good hash function should distribute the keys uniformly into the slots of the table. Regularity in the key distribution should not affect this uniformity. 12 Division method Assume all keys are integers, and define h ( k ) = k mod m . Deficiency: Don’t pick an m that has a small divisor d . A preponderance of keys that are congruent modulo d can adversely affect uniformity. 13 Division method Extreme deficiency: If m = 2 r , then the hash doesn’t even depend on all the bits of k : If k = 1011000111011010 2 and r = 6 , then h ( k ) = 011010 2 .  #### You've reached the end of your free preview.

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• Fall '05
• RudolfFleischer

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