13-Amortized-Analysis

13-Amortized-Analysis - Algorithms LECTURE 13 Amortized...

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Algorithms L13.1 Professor Ashok Subramanian L ECTURE 13 Amortized Analysis Dynamic tables Aggregate method Accounting method Potential method Algorithms
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Algorithms L13.2 How large should a hash table be? Problem: What if we don’t know the proper size in advance? Goal: Make the table as small as possible, but large enough so that it won’t overflow (or otherwise become inefficient). I DEA : Whenever the table overflows, “grow” it by allocating (via malloc or new ) a new, larger table. Move all items from the old table into the new one, and free the storage for the old table. Solution: Dynamic tables.
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Algorithms L13.3 Example of a dynamic table 1. I NSERT 1 1. I NSERT overflow
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Algorithms L13.4 1 Example of a dynamic table 1. I NSERT 1. I NSERT overflow
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Algorithms L13.5 1 2 Example of a dynamic table 1. I NSERT 1. I NSERT
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Algorithms L13.6 Example of a dynamic table 1. I NSERT 1. I NSERT 1 2 1. I NSERT overflow
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Algorithms L13.7 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 2 1 overflow
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Algorithms L13.8 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 2 1
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Algorithms L13.9 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 4 3 2 1
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Algorithms L13.10 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 4 3 2 1 overflow
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Algorithms L13.11 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 4 3 2 1 overflow
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Algorithms L13.12 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 4 3 2 1
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Algorithms L13.13 Example of a dynamic table 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 1. I NSERT 6 1. I NSERT 5 4 3 2 1 7 1. I NSERT
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L13.14 Worst-case analysis Consider a sequence of n insertions. The worst-case time to execute one insertion is Θ ( n ) . Therefore, the worst-case time for n insertions is n · Θ ( n ) = Θ ( n ) . WRONG!
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13-Amortized-Analysis - Algorithms LECTURE 13 Amortized...

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