387 research outputs found
Multi-task Regression using Minimal Penalties
In this paper we study the kernel multiple ridge regression framework, which
we refer to as multi-task regression, using penalization techniques. The
theoretical analysis of this problem shows that the key element appearing for
an optimal calibration is the covariance matrix of the noise between the
different tasks. We present a new algorithm to estimate this covariance matrix,
based on the concept of minimal penalty, which was previously used in the
single-task regression framework to estimate the variance of the noise. We
show, in a non-asymptotic setting and under mild assumptions on the target
function, that this estimator converges towards the covariance matrix. Then
plugging this estimator into the corresponding ideal penalty leads to an oracle
inequality. We illustrate the behavior of our algorithm on synthetic examples
L'intégration des nouveaux migrants : impact de la naturalisation et des formations linguistiques
En dépit de la masse grandissante de données publiques disponibles sur les populations immigrées et issues de l'immigration, les études de type quantitatif sur ces dernières sont très rares en France. En particulier et bien que le déclassement et la pauvreté des nouveaux immigrés soit un sujet de recherche désormais classique, peu de travaux s'intéressent aux moyens pour ces nouveaux migrants de s'extraire de leur désavantage économique. Nous nous proposons dans ce travail d'évaluer l'impact de deux instruments de politique migratoire susceptibles de faciliter l'intégration des immigrés : les formations linguistiques récemment mises en place dans le cadre du Contrat d'Accueil et d'Intégration et la naturalisation en tant qu'étape dans le processus d'intégration. L'enjeu méthodologique est de taille car la naturalisation comme la formation linguistique ne sont pas des traitements aléatoires : ainsi les migrants naturalisés sont positivement sélectionnés sur des caractéristiques inobservables à l'économétricien. Nous utilisons successivement une régression de discontinuité, une estimation de double différence et une estimation de panel pour neutraliser ces effets de sélection. Nous trouvons un léger impact de la formation linguistique sur le niveau en langue française des migrants mais nous n'observons pas d'effets sur des résultats plus larges tels que la probabilité de sortie du chômage, le revenu, le nombre de mois travaillés ou encore la volonté de s'installer définitivement. Avec un modèle à effets fixes, nous ne trouvons pas d'impact significatif de la naturalisation
A Constraint-directed Local Search Approach to Nurse Rostering Problems
In this paper, we investigate the hybridization of constraint programming and
local search techniques within a large neighbourhood search scheme for solving
highly constrained nurse rostering problems. As identified by the research, a
crucial part of the large neighbourhood search is the selection of the fragment
(neighbourhood, i.e. the set of variables), to be relaxed and re-optimized
iteratively. The success of the large neighbourhood search depends on the
adequacy of this identified neighbourhood with regard to the problematic part
of the solution assignment and the choice of the neighbourhood size. We
investigate three strategies to choose the fragment of different sizes within
the large neighbourhood search scheme. The first two strategies are tailored
concerning the problem properties. The third strategy is more general, using
the information of the cost from the soft constraint violations and their
propagation as the indicator to choose the variables added into the fragment.
The three strategies are analyzed and compared upon a benchmark nurse rostering
problem. Promising results demonstrate the possibility of future work in the
hybrid approach
On Improving Local Search for Unsatisfiability
Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution
A new filtering algorithm for the graph isomorphism problem
International audienceA new filtering algorithm for the graph isomorphism proble
Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results
Using constraint-based local search, we effectively model and efficiently
solve the problem of balancing the traffic demands on portions of the European
airspace while ensuring that their capacity constraints are satisfied. The
traffic demand of a portion of airspace is the hourly number of flights planned
to enter it, and its capacity is the upper bound on this number under which
air-traffic controllers can work. Currently, the only form of demand-capacity
balancing we allow is ground holding, that is the changing of the take-off
times of not yet airborne flights. Experiments with projected European flight
plans of the year 2030 show that already this first form of demand-capacity
balancing is feasible without incurring too much total delay and that it can
lead to a significantly better demand-capacity balance
A Local Search Modeling for Constrained Optimum Paths Problems (Extended Abstract)
Constrained Optimum Path (COP) problems appear in many real-life
applications, especially on communication networks. Some of these problems have
been considered and solved by specific techniques which are usually difficult
to extend. In this paper, we introduce a novel local search modeling for
solving some COPs by local search. The modeling features the compositionality,
modularity, reuse and strengthens the benefits of Constrained-Based Local
Search. We also apply the modeling to the edge-disjoint paths problem (EDP). We
show that side constraints can easily be added in the model. Computational
results show the significance of the approach
Integrating Conflict Driven Clause Learning to Local Search
This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach
for propositional satisfiability. It combines local search and conflict driven
clause learning (CDCL) scheme. Each time the local search part reaches a local
minimum, the CDCL is launched. For SAT problems it behaves like a tabu list,
whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable
sub-formula (MUS). Experimental results show good performances on many classes
of SAT instances from the last SAT competitions
Solving the Non-Crossing MAPF with CP
We introduce a new Multi-Agent Path Finding (MAPF) problem which is motivated by an industrial application. Given a fleet of robots that move on a workspace that may contain static obstacles, we must find paths from their current positions to a set of destinations, and the goal is to minimise the length of the longest path. The originality of our problem comes from the fact that each robot is attached with a cable to an anchor point, and that robots are not able to cross these cables.
We formally define the Non-Crossing MAPF (NC-MAPF) problem and show how to compute lower and upper bounds by solving well known assignment problems. We introduce a Variable Neighbourhood Search (VNS) approach for improving the upper bound, and a Constraint Programming (CP) model for solving the problem to optimality. We experimentally evaluate these approaches on randomly generated instances
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