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POHow to repeatedly insert elements into a sorted list fast
 

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    <p>I do not have formal CS training, so bear with me.</p> <p>I need to do a simulation, which can abstracted away to the following (omitting the details):</p> <blockquote> <p>We have a list of real numbers representing the times of events. In each step, we</p> <ol> <li>remove the first event, and</li> <li>as a result of "processing" it, a few other events may get inserted into the list at a strictly later time</li> </ol> <p>and repeat this many times.</p> </blockquote> <p><strong>Questions</strong></p> <p>What data structure / algorithm can I use to implement this as efficiently as possible? I need to increase the number of events/numbers in the list significantly. The priority is to make this as fast as possible for a long list.</p> <p>Since I'm doing this in C++, what data structures are already available in the STL or boost that will make it simple to implement this?</p> <hr> <p><strong>More details:</strong></p> <p>The number of events in the list is variable, but it's guaranteed to be between <code>n</code> and <code>2*n</code> where <code>n</code> is some simulation parameter. While the event times are increasing, the time-difference of the latest and earliest events is also guaranteed to be less than a constant <code>T</code>. Finally, I suspect that the density of events in time, while not constant, also has an upper and lower bound (i.e. all the events will never be strongly clustered around a single point in time)</p> <p><strong>Efforts so far:</strong></p> <p>As the title of the question says, I was thinking of using a sorted list of numbers. If I use a linked list for constant time insertion, then I have trouble finding the position where to insert new events in a fast (sublinear) way.</p> <p>Right now I am using an approximation where I divide time into buckets, and keep track of how many event are there in each bucket. Then process the buckets one-by-one as time "passes", always adding a new bucket at the end when removing one from the front, thus keeping the number of buckets constant. This is fast, but only an approximation.</p>
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    1. COChoosing a data structure depends on what kind of data you want to store in it as well as what operations you will perform on the data structure.
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    2. CODo all events have a unique timestamp, and what format is the time in?
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    3. CO@Als I just spent 20 minutes writing all that up ... it's all in the question
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