17,351 research outputs found
On the Complexity of Exact Pattern Matching in Graphs: Binary Strings and Bounded Degree
Exact pattern matching in labeled graphs is the problem of searching paths of
a graph that spell the same string as the pattern . This
basic problem can be found at the heart of more complex operations on variation
graphs in computational biology, of query operations in graph databases, and of
analysis operations in heterogeneous networks, where the nodes of some paths
must match a sequence of labels or types. We describe a simple conditional
lower bound that, for any constant , an -time or an -time algorithm for exact pattern
matching on graphs, with node labels and patterns drawn from a binary alphabet,
cannot be achieved unless the Strong Exponential Time Hypothesis (SETH) is
false. The result holds even if restricted to undirected graphs of maximum
degree three or directed acyclic graphs of maximum sum of indegree and
outdegree three. Although a conditional lower bound of this kind can be somehow
derived from previous results (Backurs and Indyk, FOCS'16), we give a direct
reduction from SETH for dissemination purposes, as the result might interest
researchers from several areas, such as computational biology, graph database,
and graph mining, as mentioned before. Indeed, as approximate pattern matching
on graphs can be solved in time, exact and approximate matching are
thus equally hard (quadratic time) on graphs under the SETH assumption. In
comparison, the same problems restricted to strings have linear time vs
quadratic time solutions, respectively, where the latter ones have a matching
SETH lower bound on computing the edit distance of two strings (Backurs and
Indyk, STOC'15).Comment: Using Lemma 12 and Lemma 13 might to be enough to prove Lemma 14.
However, the proof of Lemma 14 is correct if you assume that the graph used
in the reduction is a DAG. Hence, since the problem is already quadratic for
a DAG and a binary alphabet, it has to be quadratic also for a general graph
and a binary alphabe
Applications of Bee Colony Optimization
Many computationally difficult problems are attacked using non-exact algorithms, such as approximation algorithms and heuristics. This thesis investigates an ex- ample of the latter, Bee Colony Optimization, on both an established optimization problem in the form of the Quadratic Assignment Problem and the FireFighting problem, which has not been studied before as an optimization problem. Bee Colony Optimization is a swarm intelligence algorithm, a paradigm that has increased in popularity in recent years, and many of these algorithms are based on natural pro- cesses.
We tested the Bee Colony Optimization algorithm on the QAPLIB library of Quadratic Assignment Problem instances, which have either optimal or best known solutions readily available, and enabled us to compare the quality of solutions found by the algorithm. In addition, we implemented a couple of other well known algorithms for the Quadratic Assignment Problem and consequently we could analyse the runtime of our algorithm.
We introduce the Bee Colony Optimization algorithm for the FireFighting problem. We also implement some greedy algorithms and an Ant Colony Optimization al- gorithm for the FireFighting problem, and compare the results obtained on some randomly generated instances.
We conclude that Bee Colony Optimization finds good solutions for the Quadratic Assignment Problem, however further investigation on speedup methods is needed to improve its performance to that of other algorithms. In addition, Bee Colony Optimization is effective on small instances of the FireFighting problem, however as instance size increases the results worsen in comparison to the greedy algorithms, and more work is needed to improve the decisions made on these instances
Separations in Query Complexity Based on Pointer Functions
In 1986, Saks and Wigderson conjectured that the largest separation between
deterministic and zero-error randomized query complexity for a total boolean
function is given by the function on bits defined by a complete
binary tree of NAND gates of depth , which achieves . We show this is false by giving an example of a total
boolean function on bits whose deterministic query complexity is
while its zero-error randomized query complexity is . We further show that the quantum query complexity of the same
function is , giving the first example of a total function
with a super-quadratic gap between its quantum and deterministic query
complexities.
We also construct a total boolean function on variables that has
zero-error randomized query complexity and bounded-error
randomized query complexity . This is the first
super-linear separation between these two complexity measures. The exact
quantum query complexity of the same function is .
These two functions show that the relations and are optimal, up to poly-logarithmic factors. Further
variations of these functions give additional separations between other query
complexity measures: a cubic separation between and , a -power
separation between and , and a 4th power separation between
approximate degree and bounded-error randomized query complexity.
All of these examples are variants of a function recently introduced by
\goos, Pitassi, and Watson which they used to separate the unambiguous
1-certificate complexity from deterministic query complexity and to resolve the
famous Clique versus Independent Set problem in communication complexity.Comment: 25 pages, 6 figures. Version 3 improves separation between Q_E and
R_0 and updates reference
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