67,276 research outputs found
Convex Combinatorial Optimization
We introduce the convex combinatorial optimization problem, a far reaching
generalization of the standard linear combinatorial optimization problem. We
show that it is strongly polynomial time solvable over any edge-guaranteed
family, and discuss several applications
Regression Driven F--Transform and Application to Smoothing of Financial Time Series
In this paper we propose to extend the definition of fuzzy transform in order
to consider an interpolation of models that are richer than the standard fuzzy
transform. We focus on polynomial models, linear in particular, although the
approach can be easily applied to other classes of models. As an example of
application, we consider the smoothing of time series in finance. A comparison
with moving averages is performed using NIFTY 50 stock market index.
Experimental results show that a regression driven fuzzy transform (RDFT)
provides a smoothing approximation of time series, similar to moving average,
but with a smaller delay. This is an important feature for finance and other
application, where time plays a key role.Comment: IFSA-SCIS 2017, 5 pages, 6 figures, 1 tabl
The Development of Penetration Charges for Increasing the Efficiency of the Interventions of Fire Rescue Service Units
During building fires is often necessary to deliver nozzles with water to the desired
point of intervention and the wall or ceiling must be penetrated for energy supply and
the entrance of persons. Access openings for extinguishing are created with hand tools or
explosives, but it is a very time-consuming activity and fragmented material may endanger
persons. Another possibility is the use of charges with a water layer, which absorbs
the shock wave of the explosion at the back and at the same time significantly suppresses
the fragmentation of the building element on which the charge acts. The penetration
charge developed in two versions allows a sufficient penetration of the partitio
A tree-decomposed transfer matrix for computing exact Potts model partition functions for arbitrary graphs, with applications to planar graph colourings
Combining tree decomposition and transfer matrix techniques provides a very
general algorithm for computing exact partition functions of statistical models
defined on arbitrary graphs. The algorithm is particularly efficient in the
case of planar graphs. We illustrate it by computing the Potts model partition
functions and chromatic polynomials (the number of proper vertex colourings
using Q colours) for large samples of random planar graphs with up to N=100
vertices. In the latter case, our algorithm yields a sub-exponential average
running time of ~ exp(1.516 sqrt(N)), a substantial improvement over the
exponential running time ~ exp(0.245 N) provided by the hitherto best known
algorithm. We study the statistics of chromatic roots of random planar graphs
in some detail, comparing the findings with results for finite pieces of a
regular lattice.Comment: 5 pages, 3 figures. Version 2 has been substantially expanded.
Version 3 shows that the worst-case running time is sub-exponential in the
number of vertice
A stabilized finite element method for the two-field and three-field Stokes eigenvalue problems
In this paper, the stabilized finite element approximation of the Stokes
eigenvalue problems is considered for both the two-field
(displacement-pressure) and the three-field (stress-displacement-pressure)
formulations. The method presented is based on a subgrid scale concept, and
depends on the approximation of the unresolvable scales of the continuous
solution. In general, subgrid scale techniques consist in the addition of a
residual based term to the basic Galerkin formulation. The application of a
standard residual based stabilization method to a linear eigenvalue problem
leads to a quadratic eigenvalue problem in discrete form which is physically
inconvenient. As a distinguished feature of the present study, we take the
space of the unresolved subscales orthogonal to the finite element space, which
promises a remedy to the above mentioned complication. In essence, we put
forward that only if the orthogonal projection is used, the residual is
simplified and the use of term by term stabilization is allowed. Thus, we do
not need to put the whole residual in the formulation, and the linear
eigenproblem form is recovered properly. We prove that the method applied is
convergent, and present the error estimates for the eigenvalues and the
eigenfunctions. We report several numerical tests in order to illustrate that
the theoretical results are validated
Convex Matroid Optimization
We consider a problem of optimizing convex functionals over matroid bases. It
is richly expressive and captures certain quadratic assignment and clustering
problems. While generally NP-hard, we show it is polynomial time solvable when
a suitable parameter is restricted
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