45,949 research outputs found
Parameterized Approximation Algorithms for Bidirected Steiner Network Problems
The Directed Steiner Network (DSN) problem takes as input a directed
edge-weighted graph and a set of
demand pairs. The aim is to compute the cheapest network for
which there is an path for each . It is known
that this problem is notoriously hard as there is no
-approximation algorithm under Gap-ETH, even when parametrizing
the runtime by [Dinur & Manurangsi, ITCS 2018]. In light of this, we
systematically study several special cases of DSN and determine their
parameterized approximability for the parameter .
For the bi-DSN problem, the aim is to compute a planar
optimum solution in a bidirected graph , i.e., for every edge
of the reverse edge exists and has the same weight. This problem
is a generalization of several well-studied special cases. Our main result is
that this problem admits a parameterized approximation scheme (PAS) for . We
also prove that our result is tight in the sense that (a) the runtime of our
PAS cannot be significantly improved, and (b) it is unlikely that a PAS exists
for any generalization of bi-DSN, unless FPT=W[1].
One important special case of DSN is the Strongly Connected Steiner Subgraph
(SCSS) problem, for which the solution network needs to strongly
connect a given set of terminals. It has been observed before that for SCSS
a parameterized -approximation exists when parameterized by [Chitnis et
al., IPEC 2013]. We give a tight inapproximability result by showing that for
no parameterized -approximation algorithm exists under
Gap-ETH. Additionally we show that when restricting the input of SCSS to
bidirected graphs, the problem remains NP-hard but becomes FPT for
Signaling and indirect taxation
Commodities communicate. Consumers choose a consumption bundle both for its intrinsic characteristics and for what this bundle communicates about their qualities (or .identity.) to spectators. We investigate optimal indirect taxation when consumption choices are motivated by two sorts of concerns: intrinsic consumption and costly signaling. Optimal indirect taxes are introduced into a monotonic signaling game with a finite typespace of consumers. We provide sufficient conditions for the uniqueness of the D1 sequential equilibrium in terms of strategies. In the case of pure costly signaling, signaling goods can in equilibrium be taxed without burden and the optimal quantity taxes on these goods are infinite. When commodities serve both intrinsic consumption and signaling, optimal taxes can be characterized by a generalization of the Ramsey rule, which also deals with the distortions resulting from signaling.Optimal Taxation, Indirect Taxation, Costly Signaling, Identity.
A Manifesto for the Equifinality Thesis.
This essay discusses some of the issues involved in the identification and predictions of hydrological models given some calibration data. The reasons for the incompleteness of traditional calibration methods are discussed. The argument is made that the potential for multiple acceptable models as representations of hydrological and other environmental systems (the equifinality thesis) should be given more serious consideration than hitherto. It proposes some techniques for an extended GLUE methodology to make it more rigorous and outlines some of the research issues still to be resolved
Power Allocation Games in Wireless Networks of Multi-antenna Terminals
We consider wireless networks that can be modeled by multiple access channels
in which all the terminals are equipped with multiple antennas. The propagation
model used to account for the effects of transmit and receive antenna
correlations is the unitary-invariant-unitary model, which is one of the most
general models available in the literature. In this context, we introduce and
analyze two resource allocation games. In both games, the mobile stations
selfishly choose their power allocation policies in order to maximize their
individual uplink transmission rates; in particular they can ignore some
specified centralized policies. In the first game considered, the base station
implements successive interference cancellation (SIC) and each mobile station
chooses his best space-time power allocation scheme; here, a coordination
mechanism is used to indicate to the users the order in which the receiver
applies SIC. In the second framework, the base station is assumed to implement
single-user decoding. For these two games a thorough analysis of the Nash
equilibrium is provided: the existence and uniqueness issues are addressed; the
corresponding power allocation policies are determined by exploiting random
matrix theory; the sum-rate efficiency of the equilibrium is studied
analytically in the low and high signal-to-noise ratio regimes and by
simulations in more typical scenarios. Simulations show that, in particular,
the sum-rate efficiency is high for the type of systems investigated and the
performance loss due to the use of the proposed suboptimum coordination
mechanism is very small
Safe design and operation of tank reactors for multiple-reaction networks: uniqueness and multiplicity
A method is developed to design a tank reactor in which a network of reactions is carried out. The network is a combination of parallel and consecutive reactions. The method ensures unique operation. Dimensionless groups are used which are either representative of properties of the reaction system or exclusively of the design and operating variables. In a plot of the optimal yield vs the dimensionless operating temperature the region is indicated where operation under conditions of uniqueness is feasible. The method is illustrated with an example: the air oxidation of benzene of maleic anhydride
Projected Stochastic Gradients for Convex Constrained Problems in Hilbert Spaces
Convergence of a projected stochastic gradient algorithm is demonstrated for
convex objective functionals with convex constraint sets in Hilbert spaces. In
the convex case, the sequence of iterates converges weakly to a point
in the set of minimizers with probability one. In the strongly convex case, the
sequence converges strongly to the unique optimum with probability one. An
application to a class of PDE constrained problems with a convex objective,
convex constraint and random elliptic PDE constraints is shown. Theoretical
results are demonstrated numerically.Comment: 28 page
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