4,477 research outputs found
Axiomatizations of signed discrete Choquet integrals
We study the so-called signed discrete Choquet integral (also called
non-monotonic discrete Choquet integral) regarded as the Lov\'asz extension of
a pseudo-Boolean function which vanishes at the origin. We present
axiomatizations of this generalized Choquet integral, given in terms of certain
functional equations, as well as by necessary and sufficient conditions which
reveal desirable properties in aggregation theory
Pseudorandom Generators for Width-3 Branching Programs
We construct pseudorandom generators of seed length that -fool ordered read-once branching programs
(ROBPs) of width and length . For unordered ROBPs, we construct
pseudorandom generators with seed length . This is the first improvement for pseudorandom
generators fooling width ROBPs since the work of Nisan [Combinatorica,
1992].
Our constructions are based on the `iterated milder restrictions' approach of
Gopalan et al. [FOCS, 2012] (which further extends the Ajtai-Wigderson
framework [FOCS, 1985]), combined with the INW-generator [STOC, 1994] at the
last step (as analyzed by Braverman et al. [SICOMP, 2014]). For the unordered
case, we combine iterated milder restrictions with the generator of
Chattopadhyay et al. [CCC, 2018].
Two conceptual ideas that play an important role in our analysis are: (1) A
relabeling technique allowing us to analyze a relabeled version of the given
branching program, which turns out to be much easier. (2) Treating the number
of colliding layers in a branching program as a progress measure and showing
that it reduces significantly under pseudorandom restrictions.
In addition, we achieve nearly optimal seed-length
for the classes of: (1) read-once polynomials on
variables, (2) locally-monotone ROBPs of length and width
(generalizing read-once CNFs and DNFs), and (3) constant-width ROBPs of length
having a layer of width in every consecutive
layers.Comment: 51 page
Algorithms for the workflow satisfiability problem engineered for counting constraints
The workflow satisfiability problem (WSP) asks whether there exists an
assignment of authorized users to the steps in a workflow specification that
satisfies the constraints in the specification. The problem is NP-hard in
general, but several subclasses of the problem are known to be fixed-parameter
tractable (FPT) when parameterized by the number of steps in the specification.
In this paper, we consider the WSP with user-independent counting constraints,
a large class of constraints for which the WSP is known to be FPT. We describe
an efficient implementation of an FPT algorithm for solving this subclass of
the WSP and an experimental evaluation of this algorithm. The algorithm
iteratively generates all equivalence classes of possible partial solutions
until, whenever possible, it finds a complete solution to the problem. We also
provide a reduction from a WSP instance to a pseudo-Boolean SAT instance. We
apply this reduction to the instances used in our experiments and solve the
resulting PB SAT problems using SAT4J, a PB SAT solver. We compare the
performance of our algorithm with that of SAT4J and discuss which of the two
approaches would be more effective in practice
Contributions to the mathematical modeling of estimation of distribution algorithms and pseudo-boolean functions
134 p.Maximice o minimice una función objetivo definida sobre un espacio discreto. Dado que la mayoría de dichos problemas no pueden ser resueltos mediante una búsqueda exhaustiva, su resolución se aproxima frecuentemente mediante algoritmos heurísticos. Sin embargo, no existe ningún algoritmo que se comporte mejor que el resto de algoritmos para resolver todas las instancias de cualquier problema. Por ello, el objetivo ideal es, dado una instancia de un problema, saber cuál es el algoritmo cuya resoluciones más eficiente. Las dos líneas principales de investigación para lograr dicho objetivo son estudiar las definiciones de los problemas y las posibles instancias que cada problema puede generar y el estudio delos diseños y características de los algoritmos. En esta tesis, se han tratado ambas lineas. Por un lado,hemos estudiado las funciones pseudo-Booleanas y varios problemas binarios específicos. Por otro lado,se ha presentado un modelado matemático para estudiar Algoritmos de Estimación de Distribuciones diseñados para resolver problemas basados en permutaciones. La principal motivación ha sido seguir progresando en este campo para comprender mejor las relaciones entre los Problemas de Optimización Combinatoria y los algoritmos de optimización
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling EA Variants on OneMax and LeadingOnes
Theoretical and empirical research on evolutionary computation methods
complement each other by providing two fundamentally different approaches
towards a better understanding of black-box optimization heuristics. In
discrete optimization, both streams developed rather independently of each
other, but we observe today an increasing interest in reconciling these two
sub-branches. In continuous optimization, the COCO (COmparing Continuous
Optimisers) benchmarking suite has established itself as an important platform
that theoreticians and practitioners use to exchange research ideas and
questions. No widely accepted equivalent exists in the research domain of
discrete black-box optimization.
Marking an important step towards filling this gap, we adjust the COCO
software to pseudo-Boolean optimization problems, and obtain from this a
benchmarking environment that allows a fine-grained empirical analysis of
discrete black-box heuristics. In this documentation we demonstrate how this
test bed can be used to profile the performance of evolutionary algorithms.
More concretely, we study the optimization behavior of several EA
variants on the two benchmark problems OneMax and LeadingOnes. This comparison
motivates a refined analysis for the optimization time of the EA
on LeadingOnes
Optimal Discrete Uniform Generation from Coin Flips, and Applications
This article introduces an algorithm to draw random discrete uniform
variables within a given range of size n from a source of random bits. The
algorithm aims to be simple to implement and optimal both with regards to the
amount of random bits consumed, and from a computational perspective---allowing
for faster and more efficient Monte-Carlo simulations in computational physics
and biology. I also provide a detailed analysis of the number of bits that are
spent per variate, and offer some extensions and applications, in particular to
the optimal random generation of permutations.Comment: first draft, 22 pages, 5 figures, C code implementation of algorith
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