# Tagged Questions

Questions about algorithms that receive the input piecewise and have to make decisions, that is produce output, before having seen the whole input.

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### Term for open-ended algorithms

There are some enumeration problems which have little input, except for some termination criterion. Examples for the question at hand would be enumeration of prime numbers in ascending order ...
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### Online sorting without modifications

There is an array with $n$ places. There is a stream of $n$ unique numbers that arrive at a random order (permutation selected uniformly at random). Whenever a number arrives, we must put it ...
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### What is the fastest online sorting algorithm?

Quoting Online algorithm from Wikipedia: In computer science, an online algorithm[1] is one that can process its input piece-by-piece in a serial fashion, i.e., in the order that the input is fed ...
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### Online bipartite edge-cover problem with requirements

I have $N$ nodes $v_1,\ldots,v_N$ in one partition $X$ and $M \leq N$ nodes $u_1,\ldots,u_M$ in a different partition $Y$. I want to connect nodes in $X$ to nodes in $Y$ with edges under the following ...
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### Competitive ratio of this paging algorithm

In the paging problem, we have a cache of size $k$ and a universe of $n>k$ pages. In an online setting, we get requests for pages $p_1,p_2,\ldots p_t$, and are required to have page $p_i$ loaded ...
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### Methods for proving upper bound on a-approximiation algorithms? [closed]

I'm dealing with some simple randomized and on-line algorithms, both kind produce some lower/upper bound on quality of the output instance. For example, there's a simple randomized algorithm for the ...
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### Search a preprocessed text with online queries

We're given a fixed text, and we are presented with a series of online queries of patterns. For each query, the goal is to answer if the pattern exists in the text. Each pattern is a string, and we ...
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### Winnow versus Perceptron - Why adding irrelevant features increases L2(X) but not L∞(X)?

I saw here: http://www.cs.cmu.edu/~ninamf/ML11/lect0906.pdf Intuitively, if “n” is large but most features are irrelevant (i.e. target is sparse but examples are dense), then Winnow is better because ...