115,629 research outputs found
Communication Algorithms with Advice
We study the amount of knowledge about a communication network that must be given to its nodes in order to efficiently disseminate information. Our approach is quantitative: we investigate the minimum total number of bits of information (minimum size of advice) that has to be available to nodes, regardless of the type of information provided. We compare the size of advice needed to perform broadcast and wakeup (the latter is a broadcast in which nodes can transmit only after getting the source information), both using a linear number of messages (which is optimal). We show that the minimum size of advice permitting the wakeup with a linear number of messages in a n-node network, is Θ(nlog n), while the broadcast with a linear number of messages can be achieved with advice of size O(n). We also show that the latter size of advice is almost optimal: no advice of size o(n) can permit to broadcast with a linear number of messages. Thus a
Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges
Research on new optimization algorithms is often funded based on the
motivation that such algorithms might improve the capabilities to deal with
real-world and industrially relevant optimization challenges. Besides a huge
variety of different evolutionary and metaheuristic optimization algorithms,
also a large number of test problems and benchmark suites have been developed
and used for comparative assessments of algorithms, in the context of global,
continuous, and black-box optimization. For many of the commonly used synthetic
benchmark problems or artificial fitness landscapes, there are however, no
methods available, to relate the resulting algorithm performance assessments to
technologically relevant real-world optimization problems, or vice versa. Also,
from a theoretical perspective, many of the commonly used benchmark problems
and approaches have little to no generalization value. Based on a mini-review
of publications with critical comments, advice, and new approaches, this
communication aims to give a constructive perspective on several open
challenges and prospective research directions related to systematic and
generalizable benchmarking for black-box optimization
Implementation in Advised Strategies: Welfare Guarantees from Posted-Price Mechanisms When Demand Queries Are NP-Hard
State-of-the-art posted-price mechanisms for submodular bidders with
items achieve approximation guarantees of [Assadi and
Singla, 2019]. Their truthfulness, however, requires bidders to compute an
NP-hard demand-query. Some computational complexity of this form is
unavoidable, as it is NP-hard for truthful mechanisms to guarantee even an
-approximation for any [Dobzinski and
Vondr\'ak, 2016]. Together, these establish a stark distinction between
computationally-efficient and communication-efficient truthful mechanisms.
We show that this distinction disappears with a mild relaxation of
truthfulness, which we term implementation in advised strategies, and that has
been previously studied in relation to "Implementation in Undominated
Strategies" [Babaioff et al, 2009]. Specifically, advice maps a tentative
strategy either to that same strategy itself, or one that dominates it. We say
that a player follows advice as long as they never play actions which are
dominated by advice. A poly-time mechanism guarantees an -approximation
in implementation in advised strategies if there exists poly-time advice for
each player such that an -approximation is achieved whenever all
players follow advice. Using an appropriate bicriterion notion of approximate
demand queries (which can be computed in poly-time), we establish that (a
slight modification of) the [Assadi and Singla, 2019] mechanism achieves the
same -approximation in implementation in advised
strategies
Topology recognition with advice
In topology recognition, each node of an anonymous network has to
deterministically produce an isomorphic copy of the underlying graph, with all
ports correctly marked. This task is usually unfeasible without any a priori
information. Such information can be provided to nodes as advice. An oracle
knowing the network can give a (possibly different) string of bits to each
node, and all nodes must reconstruct the network using this advice, after a
given number of rounds of communication. During each round each node can
exchange arbitrary messages with all its neighbors and perform arbitrary local
computations. The time of completing topology recognition is the number of
rounds it takes, and the size of advice is the maximum length of a string given
to nodes.
We investigate tradeoffs between the time in which topology recognition is
accomplished and the minimum size of advice that has to be given to nodes. We
provide upper and lower bounds on the minimum size of advice that is sufficient
to perform topology recognition in a given time, in the class of all graphs of
size and diameter , for any constant . In most
cases, our bounds are asymptotically tight
Time vs. Information Tradeoffs for Leader Election in Anonymous Trees
The leader election task calls for all nodes of a network to agree on a
single node. If the nodes of the network are anonymous, the task of leader
election is formulated as follows: every node of the network must output a
simple path, coded as a sequence of port numbers, such that all these paths end
at a common node, the leader. In this paper, we study deterministic leader
election in anonymous trees.
Our aim is to establish tradeoffs between the allocated time and the
amount of information that has to be given to the nodes to
enable leader election in time in all trees for which leader election in
this time is at all possible. Following the framework of , this information (a single binary string) is provided to all
nodes at the start by an oracle knowing the entire tree. The length of this
string is called the . For an allocated time ,
we give upper and lower bounds on the minimum size of advice sufficient to
perform leader election in time .
We consider -node trees of diameter . While leader election
in time can be performed without any advice, for time we give
tight upper and lower bounds of . For time we give
tight upper and lower bounds of for even values of ,
and tight upper and lower bounds of for odd values of .
For the time interval for constant ,
we prove an upper bound of and a lower bound of
, the latter being valid whenever is odd or when
the time is at most . Finally, for time for any
constant (except for the case of very small diameters), we give
tight upper and lower bounds of
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