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Value ordering for finding all solutions: Interactions with adaptive variable ordering

Value ordering for finding all solutions: Interactions with ...

引用

17th international conference on principles and practice of constraint programming, cp 2011

作者： Mehta, Deepak O’Sullivan, Barry Quesada, Luis Cork Constraint Computation Centre University College Cork Ireland

ISBN: (纸本)9783642237850

We consider the impact of value ordering heuristics on the search effort required to find all solutions, or proving none exist, to a constraint satisfaction problem in k-way branching search. We show that when the variable ordering heuristic is adaptive, the order in which the values are assigned to variables can make a significant difference in all measures of search effort. We study in depth an open issue regarding the relative merit of traditional value heuristics, and their complements, when searching for all solutions. We also introduce a lazy version of k-way branching and study the effect of value orderings on finding all solutions when it is used. this paper motivates a new and fruitful line of research in the study of value ordering heuristics for proving unsatisfiability. © 2011, Springer-Verlag Berlin Heidelberg.

关键词： constraint satisfaction problems

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Increasing symmetry breaking by preserving target symmetries

Increasing symmetry breaking by preserving target symmetries

引用

18th international conference on principles and practice of constraint programming, cp 2012

作者： Lee, Jimmy H. M. Li, Jingying Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9783642335570

Breaking the exponential number of all symmetries of a constraint satisfaction problem is often too costly. In practice, we often aim at breaking a subset of the symmetries efficiently, which we call target symmetries. In static symmetry breaking, the goal is to post a set of constraints to break these target symmetries in order to reduce the solution set and thus also the search space. Symmetries of a problem are all intertwined. A symmetry breaking constraint intended for a particular symmetry almost always breaks more than just the intended symmetry as a side-effect. Different constraints for breaking the same target symmetry can have different side-effects. Conventional wisdom suggests that we should select a symmetry breaking constraint that has more side-effects by breaking more symmetries. While this wisdom is valid in many ways, we should be careful where the side-effects take place. A symmetry σ of a CSP P=(V,D,C) is preserved by a set of symmetry breaking constraints Csb iff σ is a symmetry of P=(V,D,CUCsb). We give theorems and examples to demonstrate that it is beneficial to post symmetry breaking constraints that preserve the target symmetries and restrict the side-effects to only non-target symmetries as much as possible. the benefits are in terms of the number of symmetries broken and the extent to which a symmetry is broken (or eliminated), resulting in a smaller solution set and search space. Extensive experiments are also conducted to confirm the feasibility and efficiency of our proposal empirically. © 2012 Springer-Verlag.

关键词： constraint satisfaction problems

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constraint-Based Software Diversification for Efficient Mitigation of Code-Reuse Attacks 26th

Constraint-Based Software Diversification for Efficient Miti...

引用

26th international conference on principles and practice of constraint programming, cp 2020

作者： Tsoupidi, Rodothea Myrsini Castañeda Lozano, Roberto Baudry, Benoit KTH Royal Institute of Technology Stockholm Sweden University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9783030584740

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality of the generated code variants. this paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. Experiments using two popular compiler benchmark suites confirm that there is a trade-off between quality of each assembly code version and diversity of the entire pool of versions. Our results show that DivCon allows users to trade between these two properties by generating diverse assembly code for a range of quality bounds. In particular, the experiments show that DivCon is able to mitigate code-reuse attacks effectively while delivering near-optimal code (optimality gap). For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extends the scope of compiler-based diversification to performance-critical and resource-constrained applications. © 2020, Springer Nature Switzerland AG.

关键词： Embedded systems

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CLab: A C++ library for fast backtrack-free interactive product configuration

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10th international conference on principles and practice of constraint programming, cp 2004

作者： Jensen, Rune M. IT University of Copenhagen Rued Langgaards Vej 7 Copenhagen S Denmark

ISBN: (纸本)3540232419

Product configuration is a successful application area of constraint programming. CLab [1,2] is an open source C++ library for building fast backtrack-free interactive product configurators. It contains functions that support a two-phase approach to interactive product configuration described by Hadzic et al. [3]. In the first phase, a Binary Decision Diagram (BDD) representing the set of valid configurations is compiled offline. In the second phase, this BDD is accessed by the online interactive product configurator. the library has two major functions: one that builds the BDD from a declarative product model (M₁), and one that computes the set of possible ways a current partial configuration can be extended to a valid product (M₂). the latter function is fast (polynomial) and used to make the interactive product configuration process complete and backtrack-free. It allows the user to choose freely between any possible continuation of the partial configuration.

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Generic SBDD using computational group theory

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9th international conference on principles and practice of constraint programming, cp 2003

作者： Gent, Ian P. Harvey, Warwick Kelsey, Tom Linton, Steve School of Computer Science University of St Andrews St Andrews FifeKY16 9SS United Kingdom IC-Parc Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom

ISBN: (纸本)3540202021

We introduce a novel approach for symmetry breaking by dominance detection (SBDD). the essence of SBDD is to perform ‘dominance checks’ at each node in a search tree to ensure that no symmetrically equivalent node has been visited before. While a highly effective technique for dealing with symmetry in constraint programs, SBDD forces a major overhead on the programmer, of writing a dominance checker for each new problem to be solved. Our novelty here is an entirely generic dominance checker. this in itself is new, as are the algorithms to implement it. It can be used for any symmetry group arising in a constraint program. A constraint programmer using our system merely has to define a small number (typically 2–6) of generating symmetries, and our system detects and breaks all resulting symmetries. Our dominance checker also performs some propagation, again generically, so that values are removed from variables if setting them would lead to a successful dominance check. We have implemented this generic SBDD and report results on its use. Our implementation easily handles problems involving 1036 symmetries, with only four permutations needed to direct the dominance checks during search. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Group theory

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Subnet generation problem: A new network routing problem

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11th international conference on principles and practice of constraint programming - cp 2005

作者： Leung, Cheuk Fun Bede Richards, Barry Kamarainen, Olli IC-Parc Imperial College London London SW7 2AZ United Kingdom

ISBN: (纸本)3540292381

We introduce a new type of network routing problem, the subnet generation problem (SGP) which is a special case of the traffic placement problem (TPP). In the TPP, given (1) a network which consists of routers and links, and (2) a set of point-to-point traffic demands to be placed including finding feasible routes, the objective is to minimize the sum of costs of the unplaced demands subject to the Quality-of-Service routing constraints. the SGP is a TPP with an extra set of constraints that restricts the combinations of demands to be placed. In our SGP, each router has a fixed amount of information-gain that is to be transmitted to every other router in the subnet. A subnet is defined as any subset of the routers in the network. this means that every router in the subnet will have exactly the same total information-gain. the objective is to find a subnet that maximizes the total information-gain: there must be a demand with a valid path between every pair of routers in the subnet. the reason for creating demands among the routers arises from the fact that every node in a selected group of routers is required to be a client and a server. the figure below shows an example of a subnet and a path solution. We describe a structure to split the problem into a master problem (select a set of demands to be placed) and a subproblem (compute a feasible set of paths for the demands selected by the master problem). To solve the SGP, we first apply a state-of-art hybrid tree search algorithm1, designed to solve the TPP. then, we explore how the subnet structure in the SGP could be used to solve the problem more efficiently. In particular, we focus on balancing search between the master problem and the subproblem to maximize the solution quality within a fixed computational time. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Problem solving

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Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds

Pseudo-tree-based incomplete algorithm for distributed const...

引用

17th international conference on principles and practice of constraint programming, cp 2011

作者： Okimoto, Tenda Joe, Yongjoon Iwasaki, Atsushi Yokoo, Makoto Faltings, Boi Kyushu University Fukuoka8190395 Japan LausanneCH-1015 Switzerland

ISBN: (纸本)9783642237850

A Distributed constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Since it is NP-hard, considering faster incomplete algorithms is necessary for large-scale applications. Most incomplete algorithms generally do not provide any guarantees on the quality of solutions. Some notable exceptions are DALO, the bounded max-sum algorithm, and ADPOP. In this paper, we develop a new solution criterion called p-optimality and an incomplete algorithm for obtaining a p-optimal solution. the characteristics of this algorithm are as follows: (i) it can provide the upper bounds of the absolute/relative errors of the solution, which can be obtained a priori/a posteriori, respectively, (ii) it is based on a pseudo-tree, which is a widely used graph structure in complete DCOP algorithms, (iii) it is a one-shot type algorithm, which runs in polynomial-time in the number of agents n assuming p is fixed, and (iv) it has adjustable parameter p, so that agents can trade-off better solution quality against computational overhead. the evaluation results illustrate that this algorithm can obtain better quality solutions and bounds compared to existing bounded incomplete algorithms, while the run time of this algorithm is shorter. © 2011, Springer-Verlag Berlin Heidelberg.

关键词： Constrained optimization

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Orchestrating satisfiability engines 17th

Orchestrating satisfiability engines

引用

17th international conference on principles and practice of constraint programming, cp 2011

作者： de Moura, Leonardo Microsoft Research One Microsoft Way RedmondWA98052 United States

ISBN: (纸本)9783642237850

constraint satisfaction problems arise in many diverse areas including software and hardware verification, type inference, static program analysis, test-case generation, scheduling, planning and graph problems. these areas share a common trait, they include a core component using logical formulas for describing states and transformations between them. the most well-known constraint satisfaction problem is propositional satisfiability, SAT, where the goal is to decide whether a formula over Boolean variables, formed using logical connectives can be made true by choosing true/false values for its variables. Some problems are more naturally described using richer languages, such as arithmetic. A supporting theory (of arithmetic) is then required to capture the meaning of these formulas. Solvers for such formulations are commonly called Satisfiability Modulo theories (SMT) solvers. Software analysis and model-based tools are increasingly complex and multi-faceted software systems. However, at their core is invariably a component using logical formulas for describing states and transformations between system states. In a nutshell, symbolic logic is the calculus of computation. the state-of-the art SMT solver, Z3, developed at Microsoft Research, can be used to check the satisfiability of logical formulas over one or more theories. SMT solvers offer a compelling match for software tools, since several common software constructs map directly into supported theories. Z3 comprises of a collection of symbolic reasoning engines. these engines are combined to address the requirements of each application domain. In this talk, we describe the main challenges in orchestrating the different engines, and the main application domains within Microsoft. © 2011, Springer-Verlag Berlin Heidelberg.

关键词： constraint satisfaction problems

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Weibull-based benchmarks for bin packing

Weibull-based benchmarks for bin packing

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18th international conference on principles and practice of constraint programming, cp 2012

作者： Castiñeiras, Ignacio De Cauwer, Milan O'Sullivan, Barry Dpto. de Sistemas Informáticos Y Computación Universidad Complutense de Madrid Spain Département Informatique Université de Nantes France Cork Constraint Computation Centre University College Cork Ireland

ISBN: (纸本)9783642335570

Bin packing is a ubiquitous problem that arises in many practical applications. the motivation for the work presented here comes from the domain of data centre optimisation. In this paper we present a parameterisable benchmark generator for bin packing instances based on the well-known Weibull distribution. Using the shape and scale parameters of this distribution we can generate benchmarks that contain a variety of item size distributions. We show that real-world bin packing benchmarks can be modelled extremely well using our approach. We also study both systematic and heuristic bin packing methods under a variety of Weibull settings. We observe that for all bin capacities, the number of bins required in an optimal solution increases as the Weibull shape parameter increases. However, for each bin capacity, there is a range of Weibull shape settings, corresponding to different item size distributions, for which bin packing is hard for a cp-based method. © 2012 Springer-Verlag.

关键词： Heuristic methods

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Non-systematic search and learning: An empirical study 4th

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4th international conference on principles and practice of constraint programming, cp 1998

作者： thomas Richards, E. Richards, Barry IC-Parc Imperial College LondonSW7 2AZ United Kingdom

ISBN: (纸本)3540652248

this paper explores the performance of a new complete nonsystematic search algorithm learn-SAT on two types of 3-SAT problems, (i) an extended range of AIM problems [1] and (ii) structured unsolvable problems [2]. these are thought to present a difficult challenge for non-systematic search algorithms. they have been extensively used to study powerful special purpose SAT algorithms. We consider two of these, viz. the tableau-based algorithm of Bayardo & Schrag [2] and relsat. We compare their performance with that of learn-SAT, which is based on restart-repair and learning no-goods. Surprisingly, learn-SAT does very well. Sometimes it does much better than the other two algorithms;at other times they are broadly equivalent;and then there are some "anomalies". One thing at least is clear, learn-SAT solves problems which many would predict are beyond its scope. the relative performance of the three algorithms generates several interesting questions. We point to some of them with a view to future research. the empirical paradigm in this paper reflect some of the views outlined by Mammen & Hogg [10]. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Learning algorithms

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