Introduction to Algorithms, Second Edition

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The University of Texas at Austin Lecture 16 Department of Computer Sciences Professor Vijaya Ramachandran Amortized Analysis CS357: ALGORITHMS, Spring 2006 1 Amortized Analysis Given a data structure that supports certain operations, amortized analysis provides an upper bound on the average cost of each operation for any sequence of a given length n (i.e., an upper bound for a worst-case sequence). By convention, amortized cost is specified as cost per operation, and not as cost of a sequence of operations of a given length. Although we talk about the ‘average cost per operation’, no probability is involved. While performing amortized analysis, we usually assume that the data structure starts from a canonical start configuration, which is typically that corresponding to the empty set. Sometimes we analyze amortized cost per operation by type of operation. There are three methods commonly used for amortized analysis: Aggregate method Accounting method Potential method All three methods give the same solution (except that the solution returned by the aggregate method does not allow for different amortized costs for different types of operations), but they approach it in different ways. Example. Consider a data structure MStack that supports the following 3 operations: Push ( S, x ): add element x to set S Pop ( S ): remove the most recently added element from S Multipop ( S, k ): let l = min( k, | S | ); remove the l most recently added elements from S We implement Mstack as a standard stack. Then, Push and Pop take constant time. But the third operation takes Θ( l ) time, since the Multipop operation takes l units of time to remove the l elements plus a constant time to access the stack and determine the value of l . Question. What is the amortized cost per operation of an MStack operation, assuming we start with the empty set S ?
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1.1 Aggregate Analysis In this type of analysis, we obtain an upper bound T ( n ) on the time needed to execute any sequence of n
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