11,855 research outputs found
The energy scheduling problem: Industrial case-study and constraint propagation techniques
This paper deals with production scheduling involving energy constraints, typically electrical energy.
We start by an industrial case-study for which we propose a two-step integer/constraint programming method. From the industrial problem we derive a generic problem,the Energy Scheduling Problem (EnSP). We propose an extension of specific resource constraint propagation techniques to efficiently prune the search space for EnSP solving. We also present a branching scheme to solve the problem via
tree search.Finally,computational results are provided
Scalable Parallel Numerical CSP Solver
We present a parallel solver for numerical constraint satisfaction problems
(NCSPs) that can scale on a number of cores. Our proposed method runs worker
solvers on the available cores and simultaneously the workers cooperate for the
search space distribution and balancing. In the experiments, we attained up to
119-fold speedup using 256 cores of a parallel computer.Comment: The final publication is available at Springe
Abstract Diagnosis for Timed Concurrent Constraint programs
The Timed Concurrent Constraint Language (tccp in short) is a concurrent
logic language based on the simple but powerful concurrent constraint paradigm
of Saraswat. In this paradigm, the notion of store-as-value is replaced by the
notion of store-as-constraint, which introduces some differences w.r.t. other
approaches to concurrency. In this paper, we provide a general framework for
the debugging of tccp programs. To this end, we first present a new compact,
bottom-up semantics for the language that is well suited for debugging and
verification purposes in the context of reactive systems. We also provide an
abstract semantics that allows us to effectively implement debugging algorithms
based on abstract interpretation. Given a tccp program and a behavior
specification, our debugging approach automatically detects whether the program
satisfies the specification. This differs from other semiautomatic approaches
to debugging and avoids the need to provide symptoms in advance. We show the
efficacy of our approach by introducing two illustrative examples. We choose a
specific abstract domain and show how we can detect that a program is
erroneous.Comment: 16 page
Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks
Finding actions that satisfy the constraints imposed by both external inputs
and internal representations is central to decision making. We demonstrate that
some important classes of constraint satisfaction problems (CSPs) can be solved
by networks composed of homogeneous cooperative-competitive modules that have
connectivity similar to motifs observed in the superficial layers of neocortex.
The winner-take-all modules are sparsely coupled by programming neurons that
embed the constraints onto the otherwise homogeneous modular computational
substrate. We show rules that embed any instance of the CSPs planar four-color
graph coloring, maximum independent set, and Sudoku on this substrate, and
provide mathematical proofs that guarantee these graph coloring problems will
convergence to a solution. The network is composed of non-saturating linear
threshold neurons. Their lack of right saturation allows the overall network to
explore the problem space driven through the unstable dynamics generated by
recurrent excitation. The direction of exploration is steered by the constraint
neurons. While many problems can be solved using only linear inhibitory
constraints, network performance on hard problems benefits significantly when
these negative constraints are implemented by non-linear multiplicative
inhibition. Overall, our results demonstrate the importance of instability
rather than stability in network computation, and also offer insight into the
computational role of dual inhibitory mechanisms in neural circuits.Comment: Accepted manuscript, in press, Neural Computation (2018
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