166 research outputs found
Relax-and-fix heuristics applied to a real-world lot-sizing and scheduling problem in the personal care consumer goods industry
This paper addresses an integrated lot-sizing and scheduling problem in the
industry of consumer goods for personal care, a very competitive market in
which the good customer service level and the cost management show up in the
competition for the clients. In this research, a complex operational
environment composed of unrelated parallel machines with limited production
capacity and sequence-dependent setup times and costs is studied. There is also
a limited finished-goods storage capacity, a characteristic not found in the
literature. Backordering is allowed but it is extremely undesirable. The
problem is described through a mixed integer linear programming formulation.
Since the problem is NP-hard, relax-and-fix heuristics with hybrid partitioning
strategies are investigated. Computational experiments with randomly generated
and also with real-world instances are presented. The results show the efficacy
and efficiency of the proposed approaches. Compared to current solutions used
by the company, the best proposed strategies yield results with substantially
lower costs, primarily from the reduction in inventory levels and better
allocation of production batches on the machines
On complexity and convergence of high-order coordinate descent algorithms
Coordinate descent methods with high-order regularized models for
box-constrained minimization are introduced. High-order stationarity asymptotic
convergence and first-order stationarity worst-case evaluation complexity
bounds are established. The computer work that is necessary for obtaining
first-order -stationarity with respect to the variables of each
coordinate-descent block is whereas the computer
work for getting first-order -stationarity with respect to all the
variables simultaneously is . Numerical examples
involving multidimensional scaling problems are presented. The numerical
performance of the methods is enhanced by means of coordinate-descent
strategies for choosing initial points
Parameter Estimation and Quantitative Parametric Linkage Analysis with GENEHUNTER-QMOD
Objective: We present a parametric method for linkage analysis of quantitative phenotypes. The method provides a test for linkage as well as an estimate of different phenotype parameters. We have implemented our new method in the program GENEHUNTER-QMOD and evaluated its properties by performing simulations. Methods: The phenotype is modeled as a normally distributed variable, with a separate distribution for each genotype. Parameter estimates are obtained by maximizing the LOD score over the normal distribution parameters with a gradient-based optimization called PGRAD method. Results: The PGRAD method has lower power to detect linkage than the variance components analysis (VCA) in case of a normal distribution and small pedigrees. However, it outperforms the VCA and Haseman-Elston regression for extended pedigrees, nonrandomly ascertained data and non-normally distributed phenotypes. Here, the higher power even goes along with conservativeness, while the VCA has an inflated type I error. Parameter estimation tends to underestimate residual variances but performs better for expectation values of the phenotype distributions. Conclusion: With GENEHUNTER-QMOD, a powerful new tool is provided to explicitly model quantitative phenotypes in the context of linkage analysis. It is freely available at http://www.helmholtz-muenchen.de/genepi/downloads. Copyright (C) 2012 S. Karger AG, Base
Guaranteed clustering and biclustering via semidefinite programming
Identifying clusters of similar objects in data plays a significant role in a
wide range of applications. As a model problem for clustering, we consider the
densest k-disjoint-clique problem, whose goal is to identify the collection of
k disjoint cliques of a given weighted complete graph maximizing the sum of the
densities of the complete subgraphs induced by these cliques. In this paper, we
establish conditions ensuring exact recovery of the densest k cliques of a
given graph from the optimal solution of a particular semidefinite program. In
particular, the semidefinite relaxation is exact for input graphs corresponding
to data consisting of k large, distinct clusters and a smaller number of
outliers. This approach also yields a semidefinite relaxation for the
biclustering problem with similar recovery guarantees. Given a set of objects
and a set of features exhibited by these objects, biclustering seeks to
simultaneously group the objects and features according to their expression
levels. This problem may be posed as partitioning the nodes of a weighted
bipartite complete graph such that the sum of the densities of the resulting
bipartite complete subgraphs is maximized. As in our analysis of the densest
k-disjoint-clique problem, we show that the correct partition of the objects
and features can be recovered from the optimal solution of a semidefinite
program in the case that the given data consists of several disjoint sets of
objects exhibiting similar features. Empirical evidence from numerical
experiments supporting these theoretical guarantees is also provided
Implementation of an Optimal First-Order Method for Strongly Convex Total Variation Regularization
We present a practical implementation of an optimal first-order method, due
to Nesterov, for large-scale total variation regularization in tomographic
reconstruction, image deblurring, etc. The algorithm applies to -strongly
convex objective functions with -Lipschitz continuous gradient. In the
framework of Nesterov both and are assumed known -- an assumption
that is seldom satisfied in practice. We propose to incorporate mechanisms to
estimate locally sufficient and during the iterations. The mechanisms
also allow for the application to non-strongly convex functions. We discuss the
iteration complexity of several first-order methods, including the proposed
algorithm, and we use a 3D tomography problem to compare the performance of
these methods. The results show that for ill-conditioned problems solved to
high accuracy, the proposed method significantly outperforms state-of-the-art
first-order methods, as also suggested by theoretical results.Comment: 23 pages, 4 figure
An artificial fish swarm filter-based Method for constrained global optimization
Ana Maria A.C. Rocha, M. Fernanda P. Costa and Edite M.G.P. Fernandes, An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization, B. Murgante, O. Gervasi, S. Mirsa, N. Nedjah, A.M. Rocha, D. Taniar, B. Apduhan (Eds.), Lecture Notes in Computer Science, Part III, LNCS 7335, pp. 57–71, Springer, Heidelberg, 2012.An artificial fish swarm algorithm based on a filter methodology
for trial solutions acceptance is analyzed for general constrained
global optimization problems. The new method uses the filter set concept
to accept, at each iteration, a population of trial solutions whenever
they improve constraint violation or objective function, relative to the
current solutions. The preliminary numerical experiments with a wellknown
benchmark set of engineering design problems show the effectiveness
of the proposed method.Fundação para a Ciência e a Tecnologia (FCT
Dificultades para codificar, relacionar y categorizar problemas verbales algebraicos: dos estudios con estudiantes de secundaria y profesores en formación
En resolución de problemas verbales por transferencia, la activación de problemas ya conocidos que sirvan de guÃa, depende de las analogÃas percibidas entre éstos y el problema a resolver. Se desarrollan dos estudios relacionados para analizar en qué caracterÃsticas se basan los estudiantes para codificar problemas y detectar sus analogÃas, en tareas de categorización (sorting). Se utilizaron técnicas cuantitativas y cualitativas combinadas. Primero se analizó cómo los estudiantes de secundaria son influidos por diferentes variables caracterÃsticas de problemas de ciencias. Una gran proporción de sujetos no fue capaz de percibir las analogÃas y diferencias adecuadas entre problemas. El segundo estudio trató de avanzar una explicación de estos resultados. El nivel académico y la familiaridad con las temáticas fueron factores significativos, pero los futuros profesores participantes mostraron demasiadas dificultades, alertando sobre la conveniencia de revisar algunos supuestos instruccionales habituales
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