6,273 research outputs found
Linear colorings of subcubic graphs
A linear coloring of a graph is a proper coloring of the vertices of the
graph so that each pair of color classes induce a union of disjoint paths. In
this paper, we prove that for every connected graph with maximum degree at most
three and every assignment of lists of size four to the vertices of the graph,
there exists a linear coloring such that the color of each vertex belongs to
the list assigned to that vertex and the neighbors of every degree-two vertex
receive different colors, unless the graph is or . This confirms
a conjecture raised by Esperet, Montassier, and Raspaud. Our proof is
constructive and yields a linear-time algorithm to find such a coloring
Sources of Superlinearity in Davenport-Schinzel Sequences
A generalized Davenport-Schinzel sequence is one over a finite alphabet that
contains no subsequences isomorphic to a fixed forbidden subsequence. One of
the fundamental problems in this area is bounding (asymptotically) the maximum
length of such sequences. Following Klazar, let Ex(\sigma,n) be the maximum
length of a sequence over an alphabet of size n avoiding subsequences
isomorphic to \sigma. It has been proved that for every \sigma, Ex(\sigma,n) is
either linear or very close to linear; in particular it is O(n
2^{\alpha(n)^{O(1)}}), where \alpha is the inverse-Ackermann function and O(1)
depends on \sigma. However, very little is known about the properties of \sigma
that induce superlinearity of \Ex(\sigma,n).
In this paper we exhibit an infinite family of independent superlinear
forbidden subsequences. To be specific, we show that there are 17 prototypical
superlinear forbidden subsequences, some of which can be made arbitrarily long
through a simple padding operation. Perhaps the most novel part of our
constructions is a new succinct code for representing superlinear forbidden
subsequences
Minimum-Cost Coverage of Point Sets by Disks
We consider a class of geometric facility location problems in which the goal
is to determine a set X of disks given by their centers (t_j) and radii (r_j)
that cover a given set of demand points Y in the plane at the smallest possible
cost. We consider cost functions of the form sum_j f(r_j), where f(r)=r^alpha
is the cost of transmission to radius r. Special cases arise for alpha=1 (sum
of radii) and alpha=2 (total area); power consumption models in wireless
network design often use an exponent alpha>2. Different scenarios arise
according to possible restrictions on the transmission centers t_j, which may
be constrained to belong to a given discrete set or to lie on a line, etc. We
obtain several new results, including (a) exact and approximation algorithms
for selecting transmission points t_j on a given line in order to cover demand
points Y in the plane; (b) approximation algorithms (and an algebraic
intractability result) for selecting an optimal line on which to place
transmission points to cover Y; (c) a proof of NP-hardness for a discrete set
of transmission points in the plane and any fixed alpha>1; and (d) a
polynomial-time approximation scheme for the problem of computing a minimum
cost covering tour (MCCT), in which the total cost is a linear combination of
the transmission cost for the set of disks and the length of a tour/path that
connects the centers of the disks.Comment: 10 pages, 4 figures, Latex, to appear in ACM Symposium on
Computational Geometry 200
A Simple and Efficient Algorithm for Nonlinear Model Predictive Control
We present PANOC, a new algorithm for solving optimal control problems
arising in nonlinear model predictive control (NMPC). A usual approach to this
type of problems is sequential quadratic programming (SQP), which requires the
solution of a quadratic program at every iteration and, consequently, inner
iterative procedures. As a result, when the problem is ill-conditioned or the
prediction horizon is large, each outer iteration becomes computationally very
expensive. We propose a line-search algorithm that combines forward-backward
iterations (FB) and Newton-type steps over the recently introduced
forward-backward envelope (FBE), a continuous, real-valued, exact merit
function for the original problem. The curvature information of Newton-type
methods enables asymptotic superlinear rates under mild assumptions at the
limit point, and the proposed algorithm is based on very simple operations:
access to first-order information of the cost and dynamics and low-cost direct
linear algebra. No inner iterative procedure nor Hessian evaluation is
required, making our approach computationally simpler than SQP methods. The
low-memory requirements and simple implementation make our method particularly
suited for embedded NMPC applications
Multipoint secant and interpolation methods with nonmonotone line search for solving systems of nonlinear equations
Multipoint secant and interpolation methods are effective tools for solving
systems of nonlinear equations. They use quasi-Newton updates for approximating
the Jacobian matrix. Owing to their ability to more completely utilize the
information about the Jacobian matrix gathered at the previous iterations,
these methods are especially efficient in the case of expensive functions. They
are known to be local and superlinearly convergent. We combine these methods
with the nonmonotone line search proposed by Li and Fukushima (2000), and study
global and superlinear convergence of this combination. Results of numerical
experiments are presented. They indicate that the multipoint secant and
interpolation methods tend to be more robust and efficient than Broyden's
method globalized in the same way
Inflated speedups in parallel simulations via malloc()
Discrete-event simulation programs make heavy use of dynamic memory allocation in order to support simulation's very dynamic space requirements. When programming in C one is likely to use the malloc() routine. However, a parallel simulation which uses the standard Unix System V malloc() implementation may achieve an overly optimistic speedup, possibly superlinear. An alternate implementation provided on some (but not all systems) can avoid the speedup anomaly, but at the price of significantly reduced available free space. This is especially severe on most parallel architectures, which tend not to support virtual memory. It is shown how a simply implemented user-constructed interface to malloc() can both avoid artificially inflated speedups, and make efficient use of the dynamic memory space. The interface simply catches blocks on the basis of their size. The problem is demonstrated empirically, and the effectiveness of the solution is shown both empirically and analytically
Hedging, arbitrage and optimality with superlinear frictions
In a continuous-time model with multiple assets described by c\`{a}dl\`{a}g
processes, this paper characterizes superhedging prices, absence of arbitrage,
and utility maximizing strategies, under general frictions that make execution
prices arbitrarily unfavorable for high trading intensity. Such frictions
induce a duality between feasible trading strategies and shadow execution
prices with a martingale measure. Utility maximizing strategies exist even if
arbitrage is present, because it is not scalable at will.Comment: Published at http://dx.doi.org/10.1214/14-AAP1043 in the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
How Much is the Whole Really More than the Sum of its Parts? 1 + 1 = 2.5: Superlinear Productivity in Collective Group Actions
In a variety of open source software projects, we document a superlinear
growth of production () as a function of the number of active
developers , with with large dispersions. For a typical
project in this class, doubling of the group size multiplies typically the
output by a factor , explaining the title. This superlinear law is
found to hold for group sizes ranging from 5 to a few hundred developers. We
propose two classes of mechanisms, {\it interaction-based} and {\it large
deviation}, along with a cascade model of productive activity, which unifies
them. In this common framework, superlinear productivity requires that the
involved social groups function at or close to criticality, in the sense of a
subtle balance between order and disorder. We report the first empirical test
of the renormalization of the exponent of the distribution of the sizes of
first generation events into the renormalized exponent of the distribution of
clusters resulting from the cascade of triggering over all generation in a
critical branching process in the non-meanfield regime. Finally, we document a
size effect in the strength and variability of the superlinear effect, with
smaller groups exhibiting widely distributed superlinear exponents, some of
them characterizing highly productive teams. In contrast, large groups tend to
have a smaller superlinearity and less variability.Comment: 29 pages, 8 figure
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