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共 323 条记录，以下是121-130 订阅

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A Complete Tolerant Algebraic Side-Channel Attack for AES with cp 1

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24th international conference on the principles and practice of constraint programming (cp)

作者： Liu, Fanghui Cruz, Waldemar Michel, Laurent Univ Connecticut Sch Engn Comp Sci & Engn Dept Storrs CT 06269 USA

ISBN: (数字)9783319983349

ISBN: (纸本)9783319983349;9783319983332

Tolerant Algebraic Side-Channel Attack (TASCA) is a combination of algebraic and side-channel analysis with error tolerance. Oren et al., used mathematical programming to implement TASCA over a round-limited version of AES. In [7], Liu et al. revisited their results and introduced a TASCA-cp model that delivers solutions to this 1-round relaxation with orders of magnitude improvement in both solving time and memory consumption. this paper extends the result and considers TASCA for the full 10-rounds AES algorithm. Two approaches are introduced: staged and integrated. the staged approach uses TASCA-cp as a spring board to enumerate and check its candidate solutions against the requirements of subsequent rounds. the integrated model formulates all the rounds of AES together with side-channel constraints on all rounds within a single unified optimization model. Empirical results shows both approaches are suitable to find the correct key of AES while the integrated model dominates the staged both in simplicity and solving time.

关键词： Algebraic side-channel attack AES Cryptography Block cipher constraint programming Optimization

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Sequential Time Splitting and Bounds Communication for a Portfolio of Optimization Solvers

Sequential Time Splitting and Bounds Communication for a Por...

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20th international conference on the principles and practice of constraint programming (cp)

作者： Amadini, Roberto Stuckey, Peter J. Univ Bologna Dept Comp Sci & Engn Lab Focus INRIA I-40126 Bologna Italy Univ Melbourne Dept Informat Syst & Comp Melbourne Vic 3010 Australia

ISBN: (纸本)9783319104287;9783319104270

Scheduling a subset of solvers belonging to a given portfolio has proven to be a good strategy when solving constraint Satisfaction Problems (CSPs). In this paper, we show that this approach can also be effective for constraint Optimization Problems (COPs). Unlike CSPs, sequential execution of optimization solvers can communicate information in the form of bounds to improve the performance of the following solvers. We provide a hybrid and flexible portfolio approach that combines static and dynamic time splitting for solving a given COP. Empirical evaluations show the approach is promising and sometimes even able to outperform the best solver of the porfolio.

关键词： constraint satisfaction problems

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Insights into Parallelism with Intensive Knowledge Sharing

Insights into Parallelism with Intensive Knowledge Sharing

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20th international conference on the principles and practice of constraint programming (cp)

作者： Sabharwal, Ashish Samulowitz, Horst Allen Inst Artificial Intelligence AI2 Seattle WA 98103 USA IBM Watson Res Ctr Yorktown Hts NY 10598 USA

ISBN: (纸本)9783319104287;9783319104270

Novel search space splitting techniques have recently been successfully exploited to paralleliz constraint programming and Mixed Integer programming solvers. We first show how universal hashing can be used to extend one such interesting approach to a generalized setting that goes beyond discrepancy-based search, while still retaining strong theoretical guarantees. We then explain that such static or explicit splitting approaches are not as effective in the context of parallel combinatorial search with intensive knowledge acquisition and sharing such as parallel SAT, where implicit splitting through clause sharing appears to dominate. Furthermore, we show that in a parallel setting there exists a surprising tradeoff between the well-known communication cost for knowledge sharing across multiple compute nodes and the so far neglected cost incurred by the computational load per node. We provide experimental evidence that one can successfully exploit this tradeoff and achieve reasonable speedups in parallel SAT solving beyond 16 cores.

关键词： constraint programming

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Solution directed backjumping for QCSP

Solution directed backjumping for QCSP

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13th international conference on principles and practice of constraint programming (cp 2007)

作者： Bacchus, Fahiem Stergiou, Kostas Univ Toronto Dept Comp Sci Toronto ON Canada Univ Aegean Dept Informat & Commun Syst Engn Mitilini Greece

ISBN: (纸本)9783540749691

In this paper we present new techniques for improving backtracking based Quantified constraint Satisfaction Problem (QCSP) solvers. QCSP is a generalization of CSP in which variables are either universally or existentially quantified and these quantifiers can be alternated in arbitrary ways. Our main new technique is solution directed backjumping (SBJ). In analogue to conflict directed backjumping, SBJ allows the solver to backtrack out of solved sub-trees without having to find all of the distinct solutions normally required to validate the universal variables. Experiments with the solver QCSP-Solve demonstrate that SBJ can improve its performance on random instances by orders of magnitude. In addition to this contribution, we demonstrate that performing varying levels of propagation for universal vs. existential variables can also be useful for enhancing performance. Finally, we discuss some techniques that are technically interesting but do not yet yield empirical improvements.

关键词： constraint theory

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Training Binarized Neural Networks Using MIP and cp 25th

Training Binarized Neural Networks Using MIP and CP

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25th international conference on the principles and practice of constraint programming (cp)

作者： Icarte, Rodrigo Toro Illanes, Leon Castro, Margarita P. Cire, Andre A. McIlraith, Sheila A. Beck, J. Christopher Univ Toronto Dept Comp Sci Toronto ON Canada Vector Inst Toronto ON Canada Univ Toronto Dept Mech & Ind Engn Toronto ON Canada Univ Toronto Scarborough Dept Management Toronto ON Canada

ISBN: (纸本)9783030300487

Binarized Neural Networks (BNNs) are an important class of neural network characterized by weights and activations restricted to the set {-1, +1}. BNNs provide simple compact descriptions and as such have a wide range of applications in low-power devices. In this paper, we investigate a model-based approach to training BNNs using constraint programming (cp), mixed-integer programming (MIP), and cp/MIP hybrids. We formulate the training problem as finding a set of weights that correctly classify the training set instances while optimizing objective functions that have been proposed in the literature as proxies for generalizability. Our experimental results on the MNIST digit recognition dataset suggest that-when training data is limited-the BNNs found by our hybrid approach generalize better than those obtained from a state-of-the-art gradient descent method. More broadly, this work enables the analysis of neural network performance based on the availability of optimal solutions and optimality bounds.

关键词： Binarized Neural Networks Machine learning constraint programming Mixed integer programming Discrete optimization

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Lower bounds for non-binary constraint optimization problems 7

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7th international conference on principles and practice of constraint programming, cp 2001

作者： Meseguer, Pedro Larrosa, Javier Sànchez, Martì IIIA-CSIC Campus UAB Bellaterra08193 Spain Dep. LSI UPC Jordi Girona Salgado 1-3 Barcelona08034 Spain

ISBN: (纸本)3540428631

the necessity of non-binary constraint satisfaction algorithms is increasing because many real problems are inherently non-binary. Considering overconstrained problems (and Partial Forward Checking as the solving algorithm), we analyze several lower bounds proposed in the binary case, extending them for the non-binary case. We show that techniques initially developed in the context of reversible DAC can be applied in the general case, to deal with constraints of any arity. We discuss some of the issues raised for non-binary lower bounds, and we study their computational complexity. We provide experimental results of the use of the new lower bounds on overconstrained random problems, including constraints with different weights. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Constrained optimization

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GAC via unit propagation

GAC via unit propagation

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13th international conference on principles and practice of constraint programming (cp 2007)

作者： Bacchus, Fahiem Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9783540749691

In this paper we argue that an attractive and potentially very general way of achieving generalized are consistency (GAC) on a constraint is by using unit propagation (UP) over a CNF encoding of the constraint. this approach to GAC offers a number of advantages over traditional constraint specific algorithms (propagators): it is easier to implement, it automatically provides incrementality and decrementality in a backtracking context, and it can provide clausal reasons to support learning and non-chronological backtracking. Although UP on standard CNF encodings of a constraint fails to achieve GAC, we show here that alternate CNF encodings can be used on which UP does achieve GAC. We provide a generic encoding applicable to any constraint. We also give structure specific encodings for the regular, among, and gen-sequence constraints on which UP can achieve GAC with the same run time bounds as previously presented propagators. Finally, we explain how a UP engine can be added to a CSP solver to achieve a seamless integration of constraints encoded in CNF and propagated via UP and those propagated via traditional constraint specific propagators.

关键词： constraint theory

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A general scheme for multiple lower bound computation in constraint optimization 7

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7th international conference on principles and practice of constraint programming, cp 2001

作者： Dechter, Rina Kask, Kalev Larrosa, Javier United States Spain

ISBN: (纸本)3540428631

Computing lower bounds to the best-cost extension of a tuple is an ubiquous task in constraint optimization. A particular case of special interest is the computation of lower bounds to all singleton tuples, since it permits domain pruning in Branch and Bound algorithms. In this paper we introduce MCTE(z), a general algorithm which allows the computation of lower bounds to arbitrary sets of tasks. Its time and accuracy grows as a function of z allowing a controlled trade off between lower bound accuracy and time and space to fit available resources. Subsequently, a specialization of MCTE(z) called MBTE(z) is tailored to computing lower bounds to singleton tuples. Preliminary experiments on Max-CSP show that using MBTE(z) to guide dynamic variable and value orderings in branch and bound yields a dramatic reduction in the search space and, for some classes of problems, this reduction is highly cost-effective producing significant time savings and is competitive against specialized algorithms for Max-CSP. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Constrained optimization

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Towards stochastic constraint programming: A study of onine multi-choice knapsack with deadlines

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7th international conference on principles and practice of constraint programming, cp 2001

作者： Benoist, thierry Bourreau, Eric Caseau, Yves Rottembourg, Benoît Bouygues e-lab 1 av. Eugène Freyssinet St Quentin en Yvelines Cedex78061 France

ISBN: (纸本)3540428631

constraint programming (cp) is a very general programming paradigm that proved its efficiency on solving complex industrial problems. Most real-life problems are stochastic in nature, which is usually taken into account through different compromises, such as applying a deterministic algorithm to the average values of the input, or performing multiple runs of simulation. Our goal in this paper is to analyze different techniques taken either from practical cp applications or from stochastic optimization approaches. We propose a benchmark issued from our industrial experience, which may be described as an Online Multi-choice Knapsack with Deadlines. this benchmark is used to test a framework with four different dynamic strategies that utilize a different combination of the stochastic and combinatorial aspects of the problem. To evaluate the expected future state of the reservations at the time horizon, we either use simulation, average values, systematic study of the most probable scenarios, or yield management techniques. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Optimization

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View-Based Propagator Derivation

View-Based Propagator Derivation

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20th international conference on the principles and practice of constraint programming (cp)

作者： Schulte, Christian Tack, Guido KTH Royal Inst Technol SCALE Stockholm Sweden Monash Univ NICTA Melbourne Vic Australia

ISBN: (纸本)9783319104287;9783319104270

When implementing a propagator for a constraint, one must decide about variants: When implementing min, should one also implement max? Should one implement linear equations both with and without coefficients? constraint variants are ubiquitous: implementing them requires considerable effort, but yields better performance. this abstract shows how to use views to derive propagator variants where derived propagators are perfect in that they inherit essential properties such as correctness and domain and bounds consistency. Techniques for systematically deriving propagators are developed, and the abstract sketches an implementation architecture for views that is independent of the underlying constraint programming system. Evaluation of views implemented in Gecode shows that derived propagators are efficient and that views often incur no overhead. Views have proven essential for implementing Gecode, substantially reducing the amount of code that needs to be written and maintained.

关键词： constraint theory

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