RMinimum-Algorithm 0.0.1
Package for the RMinimum algorithm
Requires Python: >=3.6

# RMinimum-Algorithm

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A **Python** implementation of the **RMinimum algorithm**.

The algorithm is presented in the paper **Fragile Complexity of Comparison-Based Algorithms** by Prof. Dr. Ulrich Meyer and others in 2018. 
A reworked version of it can can be found on [arXiv.org](https://arxiv.org/abs/1901.02857 "arXiv").

It introduces the algorithms *RMinimum* and *RMedian* and also the concept of *fragile complexity*, i.e. the amount of times an element has been compared during the process of the algorithm.

The package was published on **PyPI** and tested on **Travis CI**.

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Folder | Content
--- | ---
data | all experimental data as *.csv* files
docs | scientific paper presenting the algorithm (from arXiv)
jupyter | *Jupyter Notebook* validation and test files  
src | *Python* source code
tests | *PyTest* test files

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## Algorithm
In the following we present RMinimum, a randomized recursive algorithm for finding the min-
imum among n distinct elements. The algorithm has a tuning parameter k(n) controlling the
trade-off between the expected fragile complexity f_min(n) of the minimum element and the maxi-
mum expected fragile complexity f_rem(n) of the remaining none-minimum elements; if n is clear
from the context, we use k instead of k(n). Depending on the choice of k, we obtain interest-
ing trade-offs for the pair hE [fmin (n)] , max E [frem (n)]i ranging from <O(ε−1loglog(n)), O(n_ε)> to
<O(log(n)/loglog(n)), O(log(n)/loglog(n))>. Given a total ordered set X of n distinct elements,
RMinimum performs the following steps:

1. Randomly group the elements into n/2 pairwise-disjoint pairs and use one comparison for each pair to partition X into two sets L and W of equal size: W contains all winners, i.e. elements that are smaller than their partner. Set L gets the losers, who are larger than their partner and hence cannot be the global minimum.
2. Partition L into n/k random disjoint subsets L_1 , . . . , L_n/k each of size k and find the minimum element mi in each Li using a perfectly balanced tournament tree (see Theorem 1).
3. Partition W into n/k random disjoint subsets W_1 , . . . , W_n/k each of size k and filter out all elements in W_i larger than mi to obtain W_0 = U_i {w|w ∈ Wi ∧ w < mi }.
4. If |W_0| ≤ log2 (n_0) where n0 is the initial problem size, find and return the minimum using a perfectly balanced tournament tree (see Theorem 1). Otherwise recurse on W_0.

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## Complexity 
1. *Runtime:* RMinimum requires linear work w(n) = O(n).
2. Let k(n) = n_ε for 0 < ε ≤ 1/2. Then RMinimum requires E(f_min) = O(ε−1loglogn) comparisons for the minimum and E(f_rem) = O(n_ε) for the remaining elements.
3. Let k(n) = logn/loglogn. Then RMinimum requires E(f_min) = O(logn/loglogn) comparisons for the minimum and E(f_rem) = O(logn/loglogn) for the remaining elements.