200,592 research outputs found
Automatic frequency assignment for cellular telephones using constraint satisfaction techniques
We study the problem of automatic frequency assignment for cellular telephone
systems. The frequency assignment problem is viewed as the problem
to minimize the unsatisfied soft constraints in a constraint satisfaction problem
(CSP) over a finite domain of frequencies involving co-channel, adjacent
channel, and co-site constraints. The soft constraints are automatically derived
from signal strength prediction data. The CSP is solved using a generalized
graph coloring algorithm. Graph-theoretical results play a crucial
role in making the problem tractable. Performance results from a real-world
frequency assignment problem are presented.
We develop the generalized graph coloring algorithm by stepwise refinement,
starting from DSATUR and augmenting it with local propagation,
constraint lifting, intelligent backtracking, redundancy avoidance, and iterative
deepening
A Truthful Mechanism for the Generalized Assignment Problem
We propose a truthful-in-expectation, -approximation mechanism for a
strategic variant of the generalized assignment problem (GAP). In GAP, a set of
items has to be optimally assigned to a set of bins without exceeding the
capacity of any singular bin. In the strategic variant of the problem we study,
values for assigning items to bins are the private information of bidders and
the mechanism should provide bidders with incentives to truthfully report their
values. The approximation ratio of the mechanism is a significant improvement
over the approximation ratio of the existing truthful mechanism for GAP.
The proposed mechanism comprises a novel convex optimization program as the
allocation rule as well as an appropriate payment rule. To implement the convex
program in polynomial time, we propose a fractional local search algorithm
which approximates the optimal solution within an arbitrarily small error
leading to an approximately truthful-in-expectation mechanism. The presented
algorithm improves upon the existing optimization algorithms for GAP in terms
of simplicity and runtime while the approximation ratio closely matches the
best approximation ratio given for GAP when all inputs are publicly known.Comment: 18 pages, Earlier version accepted at WINE 201
Channel assignment in cellular radio
Some heuristic channel-assignment algorithms for cellular systems are described. These algorithms have yielded optimal, or near-optimal assignments, in many cases. The channel-assignment problem can be viewed as a generalized graph-coloring problem, and these algorithms have been developed, in part, by suitably adapting some of the ideas previously introduced in heuristic graph-coloring algorithms. The channel-assignment problem is formulated as a minimum-span problem, i.e. a problem wherein the requirement is to find the minimum bandwidth necessary to satisfy a given demand. Examples are presented, and algorithm performance results are discussed
Assigning channels via the meet-in-the-middle approach
We study the complexity of the Channel Assignment problem. By applying the
meet-in-the-middle approach we get an algorithm for the -bounded Channel
Assignment (when the edge weights are bounded by ) running in time
. This is the first algorithm which breaks the
barrier. We extend this algorithm to the counting variant, at the
cost of slightly higher polynomial factor.
A major open problem asks whether Channel Assignment admits a -time
algorithm, for a constant independent of . We consider a similar
question for Generalized T-Coloring, a CSP problem that generalizes \CA. We
show that Generalized T-Coloring does not admit a
-time algorithm, where is the
size of the instance.Comment: SWAT 2014: 282-29
Adaptive approach heuristics for the generalized assignment problem
The Generalized Assignment Problem consists in assigning a set of tasks to a set of agents with minimum cost. Each agent has a limited amount of a single resource and each task must be assigned to one and only one agent, requiring a certain amount of the resource of the agent. We present new metaheuristics for the generalized assignment problem based on hybrid approaches. One metaheuristic is a MAX-MIN Ant System (MMAS), an improved version of the Ant System, which was recently proposed by Stutzle and Hoos to combinatorial optimization problems, and it can be seen has an adaptive sampling algorithm that takes in consideration the experience gathered in earlier iterations of the algorithm. Moreover, the latter heuristic is combined with local search and tabu search heuristics to improve the search. A greedy randomized adaptive search heuristic (GRASP) is also proposed. Several neighborhoods are studied, including one based on ejection chains that produces good moves without increasing the computational effort. We present computational results of the comparative performance, followed by concluding remarks and ideas on future research in generalized assignment related problems.Metaheuristics, generalized assignment, local search, GRASP, tabu search, ant systems
A polyhedral approach for the generalized assignment problem.
The generalized assignment problem (GAP) consists of finding a maximal profit assignment of n jobs over m capacity constrained agents, whereby each job has to be processed by only one agent. This contribution approaches the GAP from the polyhedral point of view. A good upper bound is obtained by approximating the convex hull of the knapsack constraints in the GAP-polytope using theoretical work of Balas. Based on this result, we propose a procedure for finding close-to-optimal solutions, which gives us a lower bound. Computational results on a set of 60representative and highly capacitated problems indicate that these solutions lie within 0.06% of the optimum. After applying some preprocessing techniques and using the obtained bounds, we solve the generated instances to optimality by branch and bound within reasonable computing time.Assignment;
Mechanism Design without Money via Stable Matching
Mechanism design without money has a rich history in social choice
literature. Due to the strong impossibility theorem by Gibbard and
Satterthwaite, exploring domains in which there exist dominant strategy
mechanisms is one of the central questions in the field. We propose a general
framework, called the generalized packing problem (\gpp), to study the
mechanism design questions without payment. The \gpp\ possesses a rich
structure and comprises a number of well-studied models as special cases,
including, e.g., matroid, matching, knapsack, independent set, and the
generalized assignment problem.
We adopt the agenda of approximate mechanism design where the objective is to
design a truthful (or strategyproof) mechanism without money that can be
implemented in polynomial time and yields a good approximation to the socially
optimal solution. We study several special cases of \gpp, and give constant
approximation mechanisms for matroid, matching, knapsack, and the generalized
assignment problem. Our result for generalized assignment problem solves an
open problem proposed in \cite{DG10}.
Our main technical contribution is in exploitation of the approaches from
stable matching, which is a fundamental solution concept in the context of
matching marketplaces, in application to mechanism design. Stable matching,
while conceptually simple, provides a set of powerful tools to manage and
analyze self-interested behaviors of participating agents. Our mechanism uses a
stable matching algorithm as a critical component and adopts other approaches
like random sampling and online mechanisms. Our work also enriches the stable
matching theory with a new knapsack constrained matching model
Connections between Optimal Transport, Combinatorial Optimization and Hydrodynamics
There are well-established connections between combinatorial optimization,
optimal transport theory and Hydrodynamics, through the linear assignment
problem in combinatorics, the Monge-Kantorovich problem in optimal transport
theory and the model of inviscid, potential, pressure-less fluids in
Hydrodynamics. Here, we consider the more challenging quadratic assignment
problem (which is NP, while the linear assignment problem is just P) and find,
in some particular case, a correspondence with the problem of finding
stationary solutions of Euler's equations for incompressible fluids. For that
purpose, we introduce and analyze a suitable "gradient flow" equation.
Combining some ideas of P.-L. Lions (for the Euler equations) and
Ambrosio-Gigli-Savar\'e (for the heat equation), we provide for the initial
value problem a concept of generalized "dissipative" solutions which always
exist globally in time and are unique whenever theyare smooth
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