10,274 research outputs found
Cellular neural networks for NP-hard optimization problems
Nowadays, Cellular Neural Networks (CNN) are practically implemented in
parallel, analog computers, showing a fast developing trend. Physicist must be
aware that such computers are appropriate for solving in an elegant manner
practically important problems, which are extremely slow on the classical
digital architecture. Here, CNN is used for solving NP-hard optimization
problems on lattices. It is proved, that a CNN in which the parameters of all
cells can be separately controlled, is the analog correspondent of a
two-dimensional Ising type (Edwards-Anderson) spin-glass system. Using the
properties of CNN computers a fast optimization method can be built for such
problems. Estimating the simulation time needed for solving such NP-hard
optimization problems on CNN based computers, and comparing it with the time
needed on normal digital computers using the simulated annealing algorithm, the
results are astonishing: CNN computers would be faster than digital computers
already at 10*10 lattice sizes. Hardwares realized nowadays are of 176*144
size. Also, there seems to be no technical difficulties adapting CNN chips for
such problems and the needed local control is expected to be fully developed in
the near future
Fine-grained Search Space Classification for Hard Enumeration Variants of Subset Problems
We propose a simple, powerful, and flexible machine learning framework for
(i) reducing the search space of computationally difficult enumeration variants
of subset problems and (ii) augmenting existing state-of-the-art solvers with
informative cues arising from the input distribution. We instantiate our
framework for the problem of listing all maximum cliques in a graph, a central
problem in network analysis, data mining, and computational biology. We
demonstrate the practicality of our approach on real-world networks with
millions of vertices and edges by not only retaining all optimal solutions, but
also aggressively pruning the input instance size resulting in several fold
speedups of state-of-the-art algorithms. Finally, we explore the limits of
scalability and robustness of our proposed framework, suggesting that
supervised learning is viable for tackling NP-hard problems in practice.Comment: AAAI 201
A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters
Current LTE network is faced with a plethora of Configuration and
Optimization Parameters (COPs), both hard and soft, that are adjusted manually
to manage the network and provide better Quality of Experience (QoE). With 5G
in view, the number of these COPs are expected to reach 2000 per site, making
their manual tuning for finding the optimal combination of these parameters, an
impossible fleet. Alongside these thousands of COPs is the anticipated network
densification in emerging networks which exacerbates the burden of the network
operators in managing and optimizing the network. Hence, we propose a machine
learning-based framework combined with a heuristic technique to discover the
optimal combination of two pertinent COPs used in mobility, Cell Individual
Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key
Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio
(SINR) of all the connected users. The first part of the framework leverages
the power of machine learning to predict the KPI of interest given several
different combinations of CIO and HOM. The resulting predictions are then fed
into Genetic Algorithm (GA) which searches for the best combination of the two
mentioned parameters that yield the maximum mean SINR for all users.
Performance of the framework is also evaluated using several machine learning
techniques, with CatBoost algorithm yielding the best prediction performance.
Meanwhile, GA is able to reveal the optimal parameter setting combination more
efficiently and with three orders of magnitude faster convergence time in
comparison to brute force approach
Deep Reinforcement Learning for Resource Allocation in V2V Communications
In this article, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communication systems based on deep reinforcement
learning. Each V2V link is considered as an agent, making its own decisions to
find optimal sub-band and power level for transmission. Since the proposed
method is decentralized, the global information is not required for each agent
to make its decisions, hence the transmission overhead is small. From the
simulation results, each agent can learn how to satisfy the V2V constraints
while minimizing the interference to vehicle-to-infrastructure (V2I)
communications
A numerical investigation of the jamming transition in traffic flow on diluted planar networks
In order to develop a toy model for car's traffic in cities, in this paper we
analyze, by means of numerical simulations, the transition among fluid regimes
and a congested jammed phase of the flow of "kinetically constrained" hard
spheres in planar random networks similar to urban roads. In order to explore
as timescales as possible, at a microscopic level we implement an event driven
dynamics as the infinite time limit of a class of already existing model (e.g.
"Follow the Leader") on an Erdos-Renyi two dimensional graph, the crossroads
being accounted by standard Kirchoff density conservations. We define a
dynamical order parameter as the ratio among the moving spheres versus the
total number and by varying two control parameters (density of the spheres and
coordination number of the network) we study the phase transition. At a
mesoscopic level it respects an, again suitable adapted, version of the
Lighthill-Whitham model, which belongs to the fluid-dynamical approach to the
problem. At a macroscopic level the model seems to display a continuous
transition from a fluid phase to a jammed phase when varying the density of the
spheres (the amount of cars in a city-like scenario) and a discontinuous jump
when varying the connectivity of the underlying network.Comment: accepted in Int.J.Mod.Phys.
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
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