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Hi there!  This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science.  When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn't be stumped when asked about them.  Over the last few years, I've interviewed at several Silicon Valley startups, and also some bigger companies, like Google, Facebook, Yahoo, LinkedIn, and eBay, and each time that I prepared for an interview, I thought to myself "Why hasn't someone created a nice Big-O cheat sheet?".  So, to save all of you fine folks a ton of time, I went ahead and created one.  Enjoy! - Eric

I suggest you read the articles by Navarro mentioned in the Refences section of the Wikipedia article titled Approximate string matching . Making your decision based on actual research is always better than on suggestions by random strangers.. Especially if performance on a known set of records is important to you.

As the community has grown to over 600 million monthly active users , it’s getting harder and harder to have your work noticed, and as people turned to bots and hashtag madness to stay on the radar, Instagram began fighting back. People who over-hashtag their work have been getting “shadow banned” , and one of the most popular Instagram botting services, Instagress, was shut down earlier today .


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