Milton Babbitt (1916-2011) was a composer of twelve-tone serial music noted for creating the all-partition array. One part of the problem in generating an all-partition array requires finding a covering of a pitch-cla...
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ISBN:
(纸本)9783319449531;9783319449524
Milton Babbitt (1916-2011) was a composer of twelve-tone serial music noted for creating the all-partition array. One part of the problem in generating an all-partition array requires finding a covering of a pitch-class matrix by a collection of sets, each forming a region containing 12 distinct elements and corresponding to a distinct integer partition of 12. constraint programming (CP) is a tool for solving such combinatorial and constraint satisfaction problems. In this paper, we use CP for the first time to formalize this problem in generating an all-partition array. Solving the whole of this problem is difficult and few known solutions exist. Therefore, we propose solving two sub-problems and joining these to form a complete solution. We conclude by presenting a solution found using this method. Our solution is the first we are aware of to be discovered automatically using a computer and differs from those found by composers.
Inspired by the geometric reasoning exploited in discrete ellipsoid-based search (DEBS) from the communications literature, we develop a constraint programming (CP) approach to solve problems with strictly convex quad...
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ISBN:
(纸本)9783319449531;9783319449524
Inspired by the geometric reasoning exploited in discrete ellipsoid-based search (DEBS) from the communications literature, we develop a constraint programming (CP) approach to solve problems with strictly convex quadratic constraints. Such constraints appear in numerous applications such as modelling the ground-to-satellite distance in global positioning systems and evaluating the efficiency of a schedule with respect to quadratic objective functions. We strengthen the key aspects of the DEBS approach and implement them as combination of a global constraint and variable/value ordering heuristics in IBM ILOG CP Optimizer. Experiments on a variety of benchmark instances show significant improvement compared to the default settings and state-of-the-art performance compared to competing technologies of mixed integer programming, semi-definite programming, and mixed integer nonlinear programming.
This paper describes a learning parallel constraint programming (CP) solver designed for solving CP problems with several instances on massively parallel computing platforms comprising multi-core parallel machines or ...
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This paper describes a learning parallel constraint programming (CP) solver designed for solving CP problems with several instances on massively parallel computing platforms comprising multi-core parallel machines or Many Integrated Cores. The CP solver proposed in this work is based on a Portfolio parallelization that employs a linear reward inaction learning algorithm in order to obtain the best possible performance for a large set of instances of the same problem. The linear reward inaction algorithm enables the prediction of the number of cores to be assigned to each search strategy based on previous experiments, reducing the computing time required to solve constraint satisfaction and optimization problems. The underlying principle of the Portfolio approach is to run N sequential search strategies using N computing cores (N To N Portfolio) where each core uses its own strategy in order to perform a search that is different from strategies used by the other cores. The first strategy that finds a solution stops all other strategies. The problem with the N To N Portfolio approach is that the number of search strategies is very small compared with the current number of computing cores used by the parallel machines. However, using an internal parallelization for each search strategy, it is possible to run N parallel search using P computing cores with P >> N (N To P Portfolio). This N To P Portfolio performs suboptimally for solving different CP problems because many computing resources are wasted. To improve this Portfolio model, an adaptive N To P Portfolio was proposed, which tries to privilege the strategy that is most likely to find a solution first in order to give it more computing cores than the other strategies. However, the main problem with the adaptive Portfolio is that it loses all the learned information at the end of each search;it is designed to solve just one CP problem. Furthermore, many computational resources are wasted by both Portfolio solvers,
This paper presents an external parallelization of constraint programming (CP) search tree mixing both static and dynamic partitioning. The principle of the parallelization is to partition the CP search tree into a se...
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This paper presents an external parallelization of constraint programming (CP) search tree mixing both static and dynamic partitioning. The principle of the parallelization is to partition the CP search tree into a set of sub-trees, then assign each sub-tree to one computing core in order to perform a local search using a sequential CP solver. In this context, static partitioning consists of decomposing the CP variables domains in order to split the CP search tree into a set of disjoint sub-trees to assign them to the cores. This strategy performs well without adding an extra cost to the parallel search, but the problem is the load imbalance between computing cores. On the other hand, dynamic partitioning is based on preservation of the search state to generate, dynamically or on demand, the sub-trees that are assigned to the cores. This strategy offers good load balancing between the different computing cores, but computing overcosts appear due to the initialisation of the search when a sub-tree is migrated from one core to another. In this paper, we propose a new partitioning strategy that mixes the static and dynamic partitioning and enjoys the benefits of each strategy. This mixed partitioning is designed to run on shared and distributed memory architectures. The performances obtained are illustrated by solving the CP problems modelled using the FlatZinc format and solved using the Google OR-Tools solver on top of the parallel Bobpp framework.
作者：
Jaulin, LucOSM
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This paper deals with the simultaneous localization and mapping problem (SLAM) for a robot. The robot has to build a map of its environment while localizing itself using a partially built map. It is assumed that (i) t...
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This paper deals with the simultaneous localization and mapping problem (SLAM) for a robot. The robot has to build a map of its environment while localizing itself using a partially built map. It is assumed that (i) the map is made of point landmarks, (ii) the landmarks are indistinguishable, (iii) the only exteroceptive measurements correspond to the distance between the robot and the landmarks. This paper shows that SLAM can be cast into a constraint network the variables of which being trajectories, digraphs and subsets of Then, we show how constraint propagation can be extended to deal with such generalized constraint networks. As a result, due to the redundancy of measurements of SLAM, we demonstrate that a constraint-based approach provides an efficient backtrack-free algorithm able to solve our SLAM problem in a guaranteed way.
Context: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while mai...
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Context: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while maintaining high software quality. Finding the smallest set of valid test configurations that ensure sufficient coverage of the system's feature interactions is thus the objective of validation engineers, especially when the execution of test configurations is costly or time-consuming. However, this problem is NP-hard in general and approximation algorithms have often been used to address it in practice. Objective: In this paper, we explore an alternative exact approach based on constraint programming that will allow engineers to increase the effectiveness of configuration testing while keeping the number of configurations as low as possible. Method: Our approach consists in using a (time-aware) minimization algorithm based on constraint programming. Given the amount of time, our solution generates a minimized set of valid test configurations that ensure coverage of all pairs of feature values (a.k.a. pairwise coverage). The approach has been implemented in a tool called PACOGEN. Results: PACOGEN was evaluated on 224 feature models in comparison with the two existing tools that are based on a greedy algorithm. For 79% of 224 feature models, PACOGEN generated up to 60% fewer test configurations than the competitor tools. We further evaluated PACOGEN in the case study of an industrial video conferencing product line with a feature model of 169 features, and found 60% fewer configurations compared with the manual approach followed by test engineers. The set of test configurations generated by PACOGEN decreased the time required by test engineers in manual test configuration by 85%, increasing the feature-pairs coverage at the same time. Conclusion: Our experimental evaluation concluded that optimal time-aware minimization of pairwise-covering test configurati
Nowadays, Compressive Sensing (CS), is one of the emerging fields of research in signal processing. It aims at reconstructing a signal from a significantly reduced number of measurements. This is done by exploiting th...
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ISBN:
(纸本)9781538621233
Nowadays, Compressive Sensing (CS), is one of the emerging fields of research in signal processing. It aims at reconstructing a signal from a significantly reduced number of measurements. This is done by exploiting the sparsity property of the original signal. Since its start in 2006, CS has been applied in several domains such as wireless channel estimation, cognitive radio, and other domains. In this paper, we address a detailed overview of compressive sensing including recent CS algorithms and applications, also we propose a solution to CS limitations using constraint programming (CP) approach. Simulation results show that CP can be a new promising direction for CS.
In this article, we present a wood procurement problem that arises in Eastern Canada. We solve a multi-period wood supply planning problem, while taking into account bucking decisions. Furthermore, we present a new fo...
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In this article, we present a wood procurement problem that arises in Eastern Canada. We solve a multi-period wood supply planning problem, while taking into account bucking decisions. Furthermore, we present a new form of flexibility which allows the harvesting capacity to change from one time period to another. We study the impact of such flexibility upon the harvesting cost. We assess the performance of the problem by comparing it with a variant where the harvesting capacity is fixed during sites' harvesting. To address this problem, we develop a hybrid approach based on both constraint and mathematical programming. In the first phase, we propose a constraint programming model dealing with forest sites harvesting and bucking problems. The result of this model is used as part of an initial solution for the whole problem formulated as a mixed integer model. We test the two versions of the problem on a set of different demand instances and we compare their results.
Bike sharing systems need to be properly rebalanced to meet the demand of users and to operate successfully. However, the problem of Balancing Bike Sharing Systems (BBSS) is a demanding task: it requires the design of...
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Bike sharing systems need to be properly rebalanced to meet the demand of users and to operate successfully. However, the problem of Balancing Bike Sharing Systems (BBSS) is a demanding task: it requires the design of optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS problem by means of constraint programming (CP). First, we introduce two different CP models for the BBSS problem including two custom branching strategies that focus on the most promising routes. Second, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform an experimental evaluation of our approaches on three different benchmark sets of instances derived from real-world bike sharing systems. We show that our CP models can be easily adapted to the different benchmark problem setups, demonstrating the benefit of using constraint programming to address the BBSS problem. Furthermore, in our experimental evaluation, we see that the pure CP (branch & bound) approach outperforms the state-of-the-art MILP on large instances and that the LNS approach is competitive with other existing approaches.
We present an original approach to compute efficient mid-term fleet configurations, at the request of a Queensland-based long-haul trucking carrier. Our approach considers one year's worth of demand data, and empl...
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We present an original approach to compute efficient mid-term fleet configurations, at the request of a Queensland-based long-haul trucking carrier. Our approach considers one year's worth of demand data, and employs a constraint programming (CP) model and an adaptive large neighbourhood search (LNS) scheme to solve the underlying multi-day multi-commodity split delivery capacitated vehicle routing problem. Our solver is able to provide the decision maker with a set of Pareto-equivalent fleet setups trading off fleet efficiency against the likelihood of requiring on-hire vehicles and drivers. Moreover, the same solver can be used to solve the daily loading and routing problem. We carry out an extensive experimental analysis, comparing our approach with an equivalent mixed integer programming (MIP) formulation, and we show that our approach is a sound methodology to provide decision support for the mid- and short-term decisions of a long-haul carrier.
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