Monthly Archives: July 2014

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Princeton Univerisy Lectures: Algorithms Part I – Analysis of Algorithms(4) – Order-of-Growth Classifications

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by Kevin Wayne, Robert Sedgewick Now, fortunately when we analyze algorithms, actually not too many different functions arise and actually that property allows us to really classify algorithms according to their performance as the problem size grows. So that’s what

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Princeton Univerisy Lectures: Algorithms Part I – Analysis of Algorithms(3) – Mathematical Models

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by Kevin Wayne, Robert Sedgewick Observing what’s happening as we did in the last section it gives us a, a way to predict performance but it really doesn’t help us understand what the algorithm’s doing. So next, we’re going to

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Princeton Univerisy Lectures: Algorithms Part I – Analysis of Algorithms(2) – Observations

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by Kevin Wayne, Robert Sedgewick  Okay, so the first step is to be able to make some observations about the running time of the programs. And for analysis of algorithms that’s easier than in a lot of scientific disciplines, as

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Princeton Univerisy Lectures: Algorithms Part I – Analysis of Algorithms(1) – Introduction

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by Kevin Wayne, Robert Sedgewick  Welcome back. Today we’re going to do some math and some science. Not a lot, but we need to have a scientific basis for understanding the performance of our algorithms to properly deploy them in

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Princeton Univerisy Lectures: Algorithms Part I – Union-Find(5) Union-Find Applications

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by Kevin Wayne, Robert Sedgewick  Alright. Now that we’ve seen efficient implementations of algorithms that can solve the unifying problem for huge problem instances let’s look to see how that might be applied. There’s a huge number of applications of

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Princeton Univerisy Lectures: Algorithms Part I – Union-Find(4) Quick Union Improvements

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by Kevin Wayne, Robert Sedgewick  Okay. So, we’ve looked at the quick union and quick find algorithms. Both of which are easy to implement. But simply can’t support a huge dynamic connectivity problems. So, how are we going to do

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