arXiv subjects

Marek A. Suchenek

Publications and source records attributed to Marek A. Suchenek.

4 recordsLinked to original sources

arXiv2017

Elementary Yet Precise Worst-case Analysis of MergeSort, A short version (SV)

This paper offers two elementary yet precise derivations of an exact formula \[ W(n) = \sum_{i=1} ^{n} \lceil \lg i \rceil = n \lceil \lg n \rceil - 2^{\lceil \lg n \rceil} + 1 \] for the maximum number $ W(n) $ of comparisons of keys performed by $ {\tt MergeSort} $ on an $ n $-element array. The first of the two, due to its structural regularity, is well worth carefully studying in its own right. Close smooth bounds on $ W(n) $ are derived. It seems interesting that $ W(n) $ is linear between the points $ n = 2^{\lfloor \lg n \rfloor} $ and it linearly interpolates its own lower bound $ n \lg n - n + 1 $ between these points.

cs.DS↗

arXiv2016

Best-case Analysis of MergeSort with an Application to the Sum of Digits Problem, A manuscript (MS) v2

An exact formula \[ B(n) = \frac{n}{2}(\lfloor \lg n \rfloor + 1) - \sum _{k=0} ^{\lfloor \lg n \rfloor} 2^k Zigzag(\frac{n}{2^{k+1}}), \] where \[ Zigzag (x) = \min (x - \lfloor x \rfloor, \lceil x \rceil - x), \] for the minimal number $ B(n) $ of comparisons of keys performed by $ {\tt MergeSort} $ on an $ n $-element array is derived and analyzed. The said formula is less complex than any other known formula for the same and can be evaluated in $ O(\log ^{c}) $ time, where $ c $ is a constant. It is shown that there is no closed-form formula for the above. Since the recurrence relation for the minimal number of comparisons of keys for $ {\tt MergeSort} $ is identical with a recurrence relation for the number of 1s in binary expansions of all integers between $ 0 $ and $ n $ (exclusively), the above results extend to the sum of binary digits problem.

cs.DM↗

arXiv2015

A Complete Worst-Case Analysis of Heapsort with Experimental Verification of Its Results, A manuscript (MS)

A rigorous proof is presented that the number of comparisons of keys performed in the worst case by ${\tt Heapsort}$ on any array of size $N \geq 2$ is equal to: $ 2 (N-1)\, ( \, \lg \frac{N-1}{2} +\varepsilon \, ) - 2s_2(N) - e_2(N) + \min (\lfloor \lg (N-1) \rfloor, 2) + 6 + c, $ where $ \varepsilon $, given by: $\varepsilon = 1 + \lceil \lg \, (N-1) \rceil - \lg \, (N-1) - 2^{\lceil \lg \, (N-1) \rceil - \lg \, (N-1)} ,$ is a function of $ N $ with the minimum value 0 and and the supremum value $δ= 1 - \lg e + \lg \lg e \approx 0.0860713320559342$, $s_2(N)$ is the sum of all digits of the binary representation of $N$, $e_2(N)$ is the exponent of $2$ in the prime factorization of $N$, and $ c $ is a binary function on the set of integers defined by: $c = 1$, if $N \leq 2 ^{\lceil \lg N \rceil} - 4$, and $c = 0$, otherwise. An algorithm that generates worst-case input arrays of any size $ N \geq 2 $ for ${\tt Heapsort}$ is offered. The algorithm has been implemented in Java, runs in $O( N \log N )$ time, and allows for precise experimental verification of the above formula.

cs.DS↗

arXiv2015

Technical Notes on Complexity of the Satisfiability Problem

These notes contain, among others, a proof that the average running time of an easy solution to the satisfiability problem for propositional calculus is, under some reasonable assumptions, linear (with constant 2) in the size of the input. Moreover, some suggestions are made about criteria for tractability of complex algorithms. In particular, it is argued that the distribution of probability on the whole input space of an algorithm constitutes an non-negligible factor in estimating whether the algorithm is tractable or not.

cs.CC↗