43,625 research outputs found
Upper-bound cost analysis of a market-based algorithm applied to the initial formation problem
©2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2007 IEEE/RSJ International
Conference on Intelligent Robots and Systems, San Diego, CA, USA, Oct 29-Nov 2, 2007.DOI: 10.1109/IROS.2007.4399100In this paper, an analysis of a market-based approach applied to the Initial Formation Problem is presented. This problem tries to determine which mobile sensor should go to each position of a desired formation in order to minimize an objective. In our case, this objective is the global distance traveled by all the mobile sensors. In this analysis, a bound on the efficiency for the market-based algorithm is calculated and it is shown that the relative difference as compared with the optimal solution increases with the logarithm of the total number of mobile sensors. The theoretical results are validated with numerous simulations
Algorithms for Graph-Constrained Coalition Formation in the Real World
Coalition formation typically involves the coming together of multiple,
heterogeneous, agents to achieve both their individual and collective goals. In
this paper, we focus on a special case of coalition formation known as
Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the
agents constrains the formation of coalitions. We focus on this type of problem
given that in many real-world applications, agents may be connected by a
communication network or only trust certain peers in their social network. We
propose a novel representation of this problem based on the concept of edge
contraction, which allows us to model the search space induced by the GCCF
problem as a rooted tree. Then, we propose an anytime solution algorithm
(CFSS), which is particularly efficient when applied to a general class of
characteristic functions called functions. Moreover, we show how CFSS can
be efficiently parallelised to solve GCCF using a non-redundant partition of
the search space. We benchmark CFSS on both synthetic and realistic scenarios,
using a real-world dataset consisting of the energy consumption of a large
number of households in the UK. Our results show that, in the best case, the
serial version of CFSS is 4 orders of magnitude faster than the state of the
art, while the parallel version is 9.44 times faster than the serial version on
a 12-core machine. Moreover, CFSS is the first approach to provide anytime
approximate solutions with quality guarantees for very large systems of agents
(i.e., with more than 2700 agents).Comment: Accepted for publication, cite as "in press
Reciprocity-driven Sparse Network Formation
A resource exchange network is considered, where exchanges among nodes are
based on reciprocity. Peers receive from the network an amount of resources
commensurate with their contribution. We assume the network is fully connected,
and impose sparsity constraints on peer interactions. Finding the sparsest
exchanges that achieve a desired level of reciprocity is in general NP-hard. To
capture near-optimal allocations, we introduce variants of the Eisenberg-Gale
convex program with sparsity penalties. We derive decentralized algorithms,
whereby peers approximately compute the sparsest allocations, by reweighted l1
minimization. The algorithms implement new proportional-response dynamics, with
nonlinear pricing. The trade-off between sparsity and reciprocity and the
properties of graphs induced by sparse exchanges are examined.Comment: 19 page
Anytime coalition structure generation on synergy graphs
We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents
A probabilistic numerical method for optimal multiple switching problem and application to investments in electricity generation
In this paper, we present a probabilistic numerical algorithm combining
dynamic programming, Monte Carlo simulations and local basis regressions to
solve non-stationary optimal multiple switching problems in infinite horizon.
We provide the rate of convergence of the method in terms of the time step used
to discretize the problem, of the size of the local hypercubes involved in the
regressions, and of the truncating time horizon. To make the method viable for
problems in high dimension and long time horizon, we extend a memory reduction
method to the general Euler scheme, so that, when performing the numerical
resolution, the storage of the Monte Carlo simulation paths is not needed.
Then, we apply this algorithm to a model of optimal investment in power plants.
This model takes into account electricity demand, cointegrated fuel prices,
carbon price and random outages of power plants. It computes the optimal level
of investment in each generation technology, considered as a whole, w.r.t. the
electricity spot price. This electricity price is itself built according to a
new extended structural model. In particular, it is a function of several
factors, among which the installed capacities. The evolution of the optimal
generation mix is illustrated on a realistic numerical problem in dimension
eight, i.e. with two different technologies and six random factors
Pressure Fluctuations in Natural Gas Networks caused by Gas-Electric Coupling
The development of hydraulic fracturing technology has dramatically increased
the supply and lowered the cost of natural gas in the United States, driving an
expansion of natural gas-fired generation capacity in several electrical
inter-connections. Gas-fired generators have the capability to ramp quickly and
are often utilized by grid operators to balance intermittency caused by wind
generation. The time-varying output of these generators results in time-varying
natural gas consumption rates that impact the pressure and line-pack of the gas
network. As gas system operators assume nearly constant gas consumption when
estimating pipeline transfer capacity and for planning operations, such
fluctuations are a source of risk to their system. Here, we develop a new
method to assess this risk. We consider a model of gas networks with
consumption modeled through two components: forecasted consumption and small
spatio-temporarily varying consumption due to the gas-fired generators being
used to balance wind. While the forecasted consumption is globally balanced
over longer time scales, the fluctuating consumption causes pressure
fluctuations in the gas system to grow diffusively in time with a diffusion
rate sensitive to the steady but spatially-inhomogeneous forecasted
distribution of mass flow. To motivate our approach, we analyze the effect of
fluctuating gas consumption on a model of the Transco gas pipeline that extends
from the Gulf of Mexico to the Northeast of the United States.Comment: 10 pages, 7 figure
Diversification and Endogenous Financial Networks
We test the hypothesis that interconnections across financial institutions
can be explained by a diversification motive. This idea stems from the
empirical evidence of the existence of long-term exposures that cannot be
explained by a liquidity motive (maturity or currency mismatch). We model
endogenous interconnections of heterogenous financial institutions facing
regulatory constraints using a maximization of their expected utility. Both
theoretical and simulation-based results are compared to a stylized genuine
financial network. The diversification motive appears to plausibly explain
interconnections among key players. Using our model, the impact of regulation
on interconnections between banks -currently discussed at the Basel Committee
on Banking Supervision- is analyzed
A MIP framework for non-convex uniform price day-ahead electricity auctions
It is well-known that a market equilibrium with uniform prices often does not
exist in non-convex day-ahead electricity auctions. We consider the case of the
non-convex, uniform-price Pan-European day-ahead electricity market "PCR"
(Price Coupling of Regions), with non-convexities arising from so-called
complex and block orders. Extending previous results, we propose a new
primal-dual framework for these auctions, which has applications in both
economic analysis and algorithm design. The contribution here is threefold.
First, from the algorithmic point of view, we give a non-trivial exact (i.e.
not approximate) linearization of a non-convex 'minimum income condition' that
must hold for complex orders arising from the Spanish market, avoiding the
introduction of any auxiliary variables, and allowing us to solve market
clearing instances involving most of the bidding products proposed in PCR using
off-the-shelf MIP solvers. Second, from the economic analysis point of view, we
give the first MILP formulations of optimization problems such as the
maximization of the traded volume, or the minimization of opportunity costs of
paradoxically rejected block bids. We first show on a toy example that these
two objectives are distinct from maximizing welfare. We also recover directly a
previously noted property of an alternative market model. Third, we provide
numerical experiments on realistic large-scale instances. They illustrate the
efficiency of the approach, as well as the economics trade-offs that may occur
in practice
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