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The Recomputation Manifesto
[article]

2013
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arXiv
*
pre-print

I also thank more recent colleagues, including Edwin Brady, Chris Jefferson, Steve Linton,

arXiv:1304.3674v1
fatcat:evrgfqfy3zhjbegn2v774mc5su
*Ian*Miguel, Pete Nightingale, Karen Petrie, Aaron Quigley, and Jonathan Ward. ...*Ian**P*.*Gent*School of Computer Science, University of St Andrews, Fife, KY16 9SX, Scotland, UK. ian.gent@st-andrews.ac.uk. http://www.cs.st-andrews.ac.uk/~ipg ...##
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Reliability of Computational Experiments on Virtualised Hardware
[article]

2011
*
arXiv
*
pre-print

-A Balanced Incomplete Block Design (BIBD) problem that takes about a minute to solve, CSPLib (

arXiv:1110.6288v1
fatcat:kubk2bi5uvcfll5ea2n4g7tfba
*Gent*and Walsh, 1999) problem 028. ... Experimental evaluation To evaluate the reliability of experimental results, we used the Minion constraint solver (*Gent*et al., 2006) . We ran it on the following three problems. ...##
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Towards Reformulating Essence Specifications for Robustness
[article]

2021
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arXiv
*
pre-print

The Essence language allows a user to specify a constraint problem at a level of abstraction above that at which constraint modelling decisions are made. Essence specifications are refined into constraint models using the Conjure automated modelling tool, which employs a suite of refinement rules. However, Essence is a rich language in which there are many equivalent ways to specify a given problem. A user may therefore omit the use of domain attributes or abstract types, resulting in fewer

arXiv:2111.00821v1
fatcat:mu3efptrozdcdaakkerxw24ua4
## more »

... ulting in fewer refinement rules being applicable and therefore a reduced set of output models from which to select. This paper addresses the problem of recovering this information automatically to increase the robustness of the quality of the output constraint models in the face of variation in the input Essence specification. We present reformulation rules that can change the type of a decision variable or add attributes that shrink its domain. We demonstrate the efficacy of this approach in terms of the quantity and quality of models Conjure can produce from the transformed specification compared with the original.##
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Modelling Constraint Solver Architecture Design as a Constraint Problem
[article]

2011
*
arXiv
*
pre-print

eq (

arXiv:1110.6290v1
fatcat:fhkic2c4brbgpbvtwghaq3hzvm
*p*v w*p*r o v i d e s [ 1 ] , 0 ) r e i f y ( watched−o r ({ eq ( pvw , 1 ) , eq ( pvw , 2 ) , eq ( pvw , 3 ) } ) ,*p*v w*p*r o v i d e s [ 4 ] ) eq (*p*v w*p*r o v i d e s [ 5 ] , 0 ) eq (*p*v w ...*p*r o v i d e s [ 2 ] , 0 ) r e i f y ( watched−o r ({ eq ( pvw , 3 ) } ) ,*p*v w*p*r o*p*e r t i e s [ 2 ] ) r e i f y ( watched−o r ({ eq ( pvw , 2 ) } ) ,*p*v w*p*r o*p*e r t i e s [ 1 ] ) r e i f ...##
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Lazy Explanations for Constraint Propagators
[chapter]

2010
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Lecture Notes in Computer Science
*

Explanations are a technique for reasoning about constraint propagation, which have been applied in many learning, backjumping and user-interaction algorithms for constraint programming. To date explanations for constraints have usually been recorded eagerly when constraint propagation happens, which leads to inecient use of time and space, because many will never be used. In this paper we show that it is possible and highly eective to calculate explanations retrospectively when they are

doi:10.1007/978-3-642-11503-5_19
fatcat:h32w7yc33nhlxheq3ssvncqaze
## more »

... hen they are needed. To this end, we implement lazy explanations in a state of the art learning framework. Experimental results conrm the eectiveness of the technique: we achieve reduction in the number of explanations calculated up to a factor of 200 and reductions in overall solve time up to a factor of 5.##
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Generating custom propagators for arbitrary constraints

2014
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Artificial Intelligence
*

*Gent*and Smith also proposed a variable and value ordering that we use here. s[n] ∈ {−1, 1}. ... Moves to state 3 State 3 (Stored State A) Apply(i) =

*P*3 [i] Update(q) : Calls compose(

*P*3 , q,

*P*4 ). ...

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Conditional Symmetry Breaking
[chapter]

2005
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Lecture Notes in Computer Science
*

*Ian*

*Gent*is supported by a Royal Society of Edinburgh SEELLD/RSE Support Research Fellowship.

*Ian*Miguel is supported by a UK Royal Academy of Engineering/EPSRC Research Fellowship. ... A conditional symmetry of a CSP

*P*holds only in a sub-problem

*P*of

*P*. The conditions of the symmetry are the constraints necessary to generate

*P*from

*P*. ...

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Metamorphic Testing of Constraint Solvers
[chapter]

2018
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Lecture Notes in Computer Science
*

Then the result S Q of applying q i to S must be contained in M as q i is inflationary, and must satisfy N

doi:10.1007/978-3-319-98334-9_46
fatcat:3isau54rvzewdhyvtyhvaarxre
*P*⊆ V S Q , because N*P*⊆ S, and N*P*is a fixed point for*P*and therefore all of the*p*i . ... If N*P*is a result of applying elements of*P*to N until a fixed point is reached, and similarly for M Q , then N*P*⊆ V M Q . ...##
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Watched Literals for Constraint Propagation in Minion
[chapter]

2006
*
Lecture Notes in Computer Science
*

*Ian*Miguel is supported by a UK Royal Academy of Engineering/EPSRC Fellowship. ... Where i ranges from 1 to n, the constraints are: V [

*P*[2 * i]] = elem i V [

*P*[2 * i + 1]] = elem i

*P*[2 * i] = i +

*P*[2 * i + 1] We found all solutions to Langford's problem up to n = 8 using this model ... For each i ∈ {1, 2, . . .} the 2i th and 2i + 1 st variables in

*P*are the first and second positions of i in V . Each variable in

*P*has domain {0, 1, . . . , 2n − 1}, indexing matrices from 0. ...

##
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An empirical study of learning and forgetting constraints

2012
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AI Communications
*

%, since the best

doi:10.3233/aic-2012-0524
fatcat:pmnlmyuz7jesrml3cl2lpg7jgu
*P*% constraints must do at least*P*% of propagations. ... In Table 1 for each chosen percentage*P*, we give what percentage of the best constraints are needed to account for*P*% of overall non-branching propagation 3 . ...##
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The TSP phase transition

1996
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Artificial Intelligence
*

Eq. (1) therefore provides a poor approximation for the mean optimal tour length for small n. 1 .

doi:10.1016/s0004-3702(96)00030-6
fatcat:26tkf6e4a5bunnbgs2gu46ynv4
*P*. ...*Gent*. 7: Walsh/Arti$cial Intelligence RR (1996) [349] [350] [351] [352] [353] [354] [355] [356] [357] [358] In addition, it provides no indication of how tour lengths are distributed around the mean ...##
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The satisfiability constraint gap

1996
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Artificial Intelligence
*

Dubois,

doi:10.1016/0004-3702(95)00047-x
fatcat:vpekfo6xkvgxjbu6l2nnuvr2gq
*P*Andre, Y. Boutkhad and J. Carher, SAT versus UNSAT, Presented at the Second DIMACS Challenge Workshop (1993). 18 1 1.*P**Gent*and T. ... Walsh, Easy problems are sometimes hard, ArtijI Intell. 70 (1994) 335-34.5. 191 1.*P**Gent*and T. Walsh, The SAT phase transition, in: A.G. ...##
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Qualitative modelling via constraint programming

2014
*
Constraints
*

These definitions allow us to post qualitative constraints about peak populations ∃

doi:10.1007/s10601-014-9158-6
fatcat:5j3qjsfdmrdubb4acuaalrqki4
*p*∈ [1, . . . , n] such that ∀i >*p*, X ′ [i] < 0 ∧ ∀i <*p*, X ′ [i] > 0. ... Others are not (depending on Lipschitz conditions and whether or not*P*=*P*SP ACE [32] ). Nonlinear ODEs are strictly harder to solve as a class, and most PDEs have no closed form solution. ...##
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Qualitative Modelling via Constraint Programming: Past, Present and Future
[article]

2012
*
arXiv
*
pre-print

These definitions allow us to post qualitative constraints about peak populations ∃

arXiv:1209.3916v1
fatcat:s7w36cffofesdiga4zaxfwqt4y
*p*∈ [1, . . . , n] such that ∀i >*p*, X [i] < 0 ∧ ∀i <*p*, X [i] > 0. ... Others are not (depending on Lipschitz conditions and whether or not*P*=*P*SP ACE [29] ). Nonlinear ODEs are strictly harder to solve as a class, and most PDEs have no closed form solution. ...##
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Generating Special-Purpose Stateless Propagators for Arbitrary Constraints
[chapter]

2010
*
Lecture Notes in Computer Science
*

*Gent*and Smith identified 7 symmetric images of the sequence [13] . We use these to post 7 symmetry-breaking constraints on s. ... Case Study: Low Autocorrelation Binary Sequences The Low Autocorrelation Binary Sequence (LABS) problem is described by

*Gent*and Smith [13] . ...

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