Algorithms and Data Structures: The Basic Toolbox

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The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary.

For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions. SlideShare Explore Search You. Submit Search. Successfully reported this slideshow. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads.

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The good news is there are better algorithms out there to compute this, so you won't be doing homework for the rest of your life and then some :. Before introducing Big O, let's talk a bit why measuring the true performance of an algorithm can be difficult. The true performance would require the answer to a few questions that leads to many messy details, variances, and unknown variables when determining how fast an algorithm runs.

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We would need to know:. Most of the time you don't know these details. This led to those in the computer science community figuring out a better way. Asymptotic Notation.

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This new notation was created so we had a language to talk about algorithms, without having to be absolutely precise in run-times, and using the knowledge that many changes in algorithm speed are by constant multiples. It really boils down to, how does run time scale with the size of input. What we really care about is - how long does this algorithm take with different inputs over time? It's not perfect when needing a detailed analysis, but does provide benefit for estimations of common algorithms like sorting to quickly see runtime speeds.

If you're interested in the most common notations used, and a few data structure speeds - check out these charts on Hacker Earth! The professor gives the following disclaimer for the activity of solving algorithms. Week two was still the introductory material for the course, so for week three, I'm hoping to dive more into Greedy Algorithms with the best processes for solving them.

The blogs should start becoming more valuable too, as there will be more code and examples compared to theory!! Collabify - An online meeting app that runs in your browser. Built by two students.

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Navigation menu. Why Algorithms Matter I previously mentioned the reasons algorithm run time is vitally important within our daily lives in A Rule of Thumb For Calculating an Algorithms Worst Case in Big oh Here's the quote that bares repeating: If computers and software didn't take time to analyze and optimize the algorithms that run our daily lives, we might never actually get those directions we asked for from Google Maps, Mint might not actually refresh our account categorization, or Twitter wouldn't show the next big POTUS tweet.

The next question then is this, how do we measure algorithms? Asymptotic Notation The true performance would require the answer to a few questions that leads to many messy details, variances, and unknown variables when determining how fast an algorithm runs.

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We would need to know: What is the speed of the computer we're running this on? What is the system architecture of the computer matters and how long specific operations would take to run Which type of compiler is being used? How does the compiler optimize and run statements?

What type of memory hierarchy are we working with and how much memory do we have? Are we using caches or hard disc look ups? Big O Notation Big O is a specific type of asymptotic notation that clarifies a few things: Growth rate - we can see how does runtimes scale with input size Communicates growth rates using the same notation between different algorithms We don't need specific constant multiples We can ignore fine details of the system It's not perfect when needing a detailed analysis, but does provide benefit for estimations of common algorithms like sorting to quickly see runtime speeds.

Writing Efficient Algorithms The process for creating highly efficient algorithms can be boiled down to three steps.

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