A Markov Chain Monte Carlo Sampler for Mixed Boolean/Integer Constraints

Kitchen, Nathan; Kuehlmann, Andreas

doi:10.1007/978-3-642-02658-4_34

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…Kitchen and Kuehlmann [16] describe a MCMC based algorithm for sampling solutions to mixed boolean and finite domain CSPs. Their algorithm starts from a random assignment and then performs a Metropolis-Hastings move to a neighboring assignment.…”

Section: The Constraint Satisfaction Problemsmentioning

confidence: 99%

Querying uncertain data with aggregate constraints

Yang

Wang

Chen

et al. 2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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Data uncertainty arises in many situations. A common approach to query processing uncertain data is to sample many "possible worlds" from the uncertain data and to run queries against the possible worlds. However, sampling is not a trivial task, as a randomly sampled possible world may not satisfy known constraints imposed on the data. In this paper, we focus on an important category of constraints, the aggregate constraints. An aggregate constraint is placed on a set of records instead of on a single record, and a reallife system usually has a large number of aggregate constraints. It is a challenging task to find qualified possible worlds in this scenario, since tuple by tuple sampling is extremely inefficient because it rarely leads to a qualified possible world. In this paper, we introduce two approaches for querying uncertain data with aggregate constraints: constraint aware sampling and MCMC sampling. Our experiments show that the new approaches lead to high quality query results with reasonable cost.

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Section: The Constraint Satisfaction Problemsmentioning

confidence: 99%

Querying uncertain data with aggregate constraints

Yang

Wang

Chen

et al. 2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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show abstract

“…Such runtime overheads cannot be neglected since it re-computes ranges when generating every pattern. In addition, the rangereduction mechanism results in a biased distribution for constraints without ordering, which is discussed in [12] [15].…”

Section: Introductionmentioning

confidence: 99%

“…There exist several studies focusing on both pattern generation speed and distribution evenness [2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A robust general constrained random pattern generator for constraints with variable ordering

Huang

2012

Proceedings of the International Conference on Computer-Aided Design

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Constrained random verification (CRV) methodology has been identified as an efficient solution to functional verification challenges. In practical cases, it is required to implement constraints with variable ordering to put essential efforts on cared patterns. However, handling constraints with variable ordering may encounter performance degradation of pattern generation speed and distribution. To resolve these challenges, we provide a preprocessing technique to analyze solution space by adaptively splitting ranges of variables and prove the feasibility of each subspace. This analysis allows us to perform effective range-reduction to enhance pattern generation speed and ensure the desired distribution. From the experimental results, our framework outperforms a state-of-art tool with 10X speedup in average and retains better stability of performance with the increase of number of variable orders.

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“…The previous studies [5][6][7][8][9][10][11][12][13][14][15][16] are restricted to a single set of constraints and may cause either distribution violation or serious speed degradation if treating various sets of constraints independently.…”

Section: Introductionmentioning

confidence: 99%

A robust constraint solving framework for multiple constraint sets in constrained random verification

Wu¹,

Huang²

2013

Proceedings of the 50th Annual Design Automation Conference

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To verify system-wide properties on SoC designs in Constrained Random Verification (CRV), the default set of constraints to generate patterns could be overridden frequently through the complex testbench. It usually results in the degradation of pattern generation speed because of low hit-rate problems. In this paper, we propose a technique to preprocess the solution space under each constraint set. Regarding the similarity between constraint sets, the infeasible subspaces under a constraint set help identify the infeasible subspaces under another constraint set. The profiled results under each constraint set are then stored in a distinct range-splitting tree (RS-Tree). These trees accelerate pattern generation under multiple constraint sets and, simultaneously, ensure the produced patterns are evenly-distributed. In our experiments, our framework achieved 10X faster pattern generation speed than a state-of-art tool in average.

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A Markov Chain Monte Carlo Sampler for Mixed Boolean/Integer Constraints

Cited by 8 publications

References 13 publications

Querying uncertain data with aggregate constraints

Querying uncertain data with aggregate constraints

A robust general constrained random pattern generator for constraints with variable ordering

A robust constraint solving framework for multiple constraint sets in constrained random verification

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