117,347 research outputs found
Quantum Amplitude Amplification and Estimation
Consider a Boolean function that partitions set
between its good and bad elements, where is good if and bad
otherwise. Consider also a quantum algorithm such that is a quantum superposition of the
elements of , and let denote the probability that a good element is
produced if is measured. If we repeat the process of running ,
measuring the output, and using to check the validity of the result, we
shall expect to repeat times on the average before a solution is found.
*Amplitude amplification* is a process that allows to find a good after an
expected number of applications of and its inverse which is proportional to
, assuming algorithm makes no measurements. This is a
generalization of Grover's searching algorithm in which was restricted to
producing an equal superposition of all members of and we had a promise
that a single existed such that . Our algorithm works whether or
not the value of is known ahead of time. In case the value of is known,
we can find a good after a number of applications of and its inverse
which is proportional to even in the worst case. We show that this
quadratic speedup can also be obtained for a large family of search problems
for which good classical heuristics exist. Finally, as our main result, we
combine ideas from Grover's and Shor's quantum algorithms to perform amplitude
estimation, a process that allows to estimate the value of . We apply
amplitude estimation to the problem of *approximate counting*, in which we wish
to estimate the number of such that . We obtain optimal
quantum algorithms in a variety of settings.Comment: 32 pages, no figure
Worst-Case Efficient Sorting with QuickMergesort
The two most prominent solutions for the sorting problem are Quicksort and
Mergesort. While Quicksort is very fast on average, Mergesort additionally
gives worst-case guarantees, but needs extra space for a linear number of
elements. Worst-case efficient in-place sorting, however, remains a challenge:
the standard solution, Heapsort, suffers from a bad cache behavior and is also
not overly fast for in-cache instances.
In this work we present median-of-medians QuickMergesort (MoMQuickMergesort),
a new variant of QuickMergesort, which combines Quicksort with Mergesort
allowing the latter to be implemented in place. Our new variant applies the
median-of-medians algorithm for selecting pivots in order to circumvent the
quadratic worst case. Indeed, we show that it uses at most
comparisons for large enough.
We experimentally confirm the theoretical estimates and show that the new
algorithm outperforms Heapsort by far and is only around 10% slower than
Introsort (std::sort implementation of stdlibc++), which has a rather poor
guarantee for the worst case. We also simulate the worst case, which is only
around 10% slower than the average case. In particular, the new algorithm is a
natural candidate to replace Heapsort as a worst-case stopper in Introsort
Solving Hard Computational Problems Efficiently: Asymptotic Parametric Complexity 3-Coloring Algorithm
Many practical problems in almost all scientific and technological
disciplines have been classified as computationally hard (NP-hard or even
NP-complete). In life sciences, combinatorial optimization problems frequently
arise in molecular biology, e.g., genome sequencing; global alignment of
multiple genomes; identifying siblings or discovery of dysregulated pathways.In
almost all of these problems, there is the need for proving a hypothesis about
certain property of an object that can be present only when it adopts some
particular admissible structure (an NP-certificate) or be absent (no admissible
structure), however, none of the standard approaches can discard the hypothesis
when no solution can be found, since none can provide a proof that there is no
admissible structure. This article presents an algorithm that introduces a
novel type of solution method to "efficiently" solve the graph 3-coloring
problem; an NP-complete problem. The proposed method provides certificates
(proofs) in both cases: present or absent, so it is possible to accept or
reject the hypothesis on the basis of a rigorous proof. It provides exact
solutions and is polynomial-time (i.e., efficient) however parametric. The only
requirement is sufficient computational power, which is controlled by the
parameter . Nevertheless, here it is proved that the
probability of requiring a value of to obtain a solution for a
random graph decreases exponentially: , making
tractable almost all problem instances. Thorough experimental analyses were
performed. The algorithm was tested on random graphs, planar graphs and
4-regular planar graphs. The obtained experimental results are in accordance
with the theoretical expected results.Comment: Working pape
Fully Dynamic Single-Source Reachability in Practice: An Experimental Study
Given a directed graph and a source vertex, the fully dynamic single-source
reachability problem is to maintain the set of vertices that are reachable from
the given vertex, subject to edge deletions and insertions. It is one of the
most fundamental problems on graphs and appears directly or indirectly in many
and varied applications. While there has been theoretical work on this problem,
showing both linear conditional lower bounds for the fully dynamic problem and
insertions-only and deletions-only upper bounds beating these conditional lower
bounds, there has been no experimental study that compares the performance of
fully dynamic reachability algorithms in practice. Previous experimental
studies in this area concentrated only on the more general all-pairs
reachability or transitive closure problem and did not use real-world dynamic
graphs.
In this paper, we bridge this gap by empirically studying an extensive set of
algorithms for the single-source reachability problem in the fully dynamic
setting. In particular, we design several fully dynamic variants of well-known
approaches to obtain and maintain reachability information with respect to a
distinguished source. Moreover, we extend the existing insertions-only or
deletions-only upper bounds into fully dynamic algorithms. Even though the
worst-case time per operation of all the fully dynamic algorithms we evaluate
is at least linear in the number of edges in the graph (as is to be expected
given the conditional lower bounds) we show in our extensive experimental
evaluation that their performance differs greatly, both on generated as well as
on real-world instances
Average-Case Complexity
We survey the average-case complexity of problems in NP.
We discuss various notions of good-on-average algorithms, and present
completeness results due to Impagliazzo and Levin. Such completeness results
establish the fact that if a certain specific (but somewhat artificial) NP
problem is easy-on-average with respect to the uniform distribution, then all
problems in NP are easy-on-average with respect to all samplable distributions.
Applying the theory to natural distributional problems remain an outstanding
open question. We review some natural distributional problems whose
average-case complexity is of particular interest and that do not yet fit into
this theory.
A major open question whether the existence of hard-on-average problems in NP
can be based on the PNP assumption or on related worst-case assumptions.
We review negative results showing that certain proof techniques cannot prove
such a result. While the relation between worst-case and average-case
complexity for general NP problems remains open, there has been progress in
understanding the relation between different ``degrees'' of average-case
complexity. We discuss some of these ``hardness amplification'' results
A practical approximation algorithm for solving massive instances of hybridization number for binary and nonbinary trees
Reticulate events play an important role in determining evolutionary
relationships. The problem of computing the minimum number of such events to
explain discordance between two phylogenetic trees is a hard computational
problem. Even for binary trees, exact solvers struggle to solve instances with
reticulation number larger than 40-50. Here we present CycleKiller and
NonbinaryCycleKiller, the first methods to produce solutions verifiably close
to optimality for instances with hundreds or even thousands of reticulations.
Using simulations, we demonstrate that these algorithms run quickly for large
and difficult instances, producing solutions that are very close to optimality.
As a spin-off from our simulations we also present TerminusEst, which is the
fastest exact method currently available that can handle nonbinary trees: this
is used to measure the accuracy of the NonbinaryCycleKiller algorithm. All
three methods are based on extensions of previous theoretical work and are
publicly available. We also apply our methods to real data
Improved Shellsort for the Worst-Case, the Best-Case and a Subset of the Average-Case Scenarios
Sorting involves rearrangement of items into ascending or descending order. There are several sorting algorithms but some are more efficient than others in terms of speed and memory utilization. Shellsort improves on Insertion sort by
decreasing the number of comparisons made on the items to be sorted. This paper presents an Improved Shellsort algorithm that further decreases the number of comparisons made on the items to be sorted through a modified
diminishing increment sort. The results obtained from the implementation of both Shellsort and the proposed algorithm shows that the proposed algorithm has a fewer number of
comparisons made for all input sizes of the best and worst cases and for input size of twenty or less for the average case. By implication, this means that the proposed algorithm is faster in these situations. The strength of the algorithm however diminishes for only the average
case of input size greater than twenty
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