Simple algorithm portfolio for SAT

Nikolić, Mladen; Marić, Filip; Janiăčić, Predrag

doi:10.1007/s10462-011-9290-2

Cited by 21 publications

(19 citation statements)

References 14 publications

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“…MedleySolver differs from SATZilla in that it targets SMT, learns solver orders, distributes time among solvers, and does not require training. ArgoSmArT k-NN [32] applies a pre-trained k-Nearest-Neighbor algorithm to portfolio SAT solving. Given a query, they deploy the most successful solver on the k nearest neighbors.…”

Section: Related Workmentioning

confidence: 99%

MedleySolver: Online SMT Algorithm Selection

Pimpalkhare

Mora

Polgreen

et al. 2021

Theory and Applications of Satisfiability Testing – SAT 2021

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Satisfiability modulo theories (SMT) solvers implement a wide range of optimizations that are often tailored to a particular class of problems, and that differ significantly between solvers. As a result, one solver may solve a query quickly while another might be flummoxed completely. Predicting the performance of a given solver is difficult for users of SMT-driven applications, particularly when the problems they have to solve do not fall neatly into a well-understood category. In this paper, we propose an online algorithm selection framework for SMT called MedleySolver that predicts the relative performances of a set of SMT solvers on a given query, distributes time amongst the solvers, and deploys the solvers in sequence until a solution is obtained. We evaluate MedleySolver against the best available alternative, an offline learning technique, in terms of pure performance and practical usability for a typical SMT user. We find that with no prior training, MedleySolver solves 93.9% of the queries solved by the virtual best solver selector achieving 59.8% of the par-2 score of the most successful individual solver, which solves 87.3%. For comparison, the best available alternative takes longer to train than MedleySolver takes to solve our entire set of 2000 queries.

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Section: Related Workmentioning

confidence: 99%

MedleySolver: Online SMT Algorithm Selection

Pimpalkhare

Mora

Polgreen

et al. 2021

Theory and Applications of Satisfiability Testing – SAT 2021

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“…Task-independent meta-learning (Abdulrahman et al 2018), which simply identifies the globally best model on historical tasks, applies to the unsupervised setting as well as OD. This can be refined by identifying the best model on similar tasks, where task similarity is measured in the metafeature space via clustering (Kadioglu et al 2010) or nearest neighbors (Nikolić, Marić, and Janičić 2013).…”

Section: Model Selection Automl Meta-learningmentioning

confidence: 99%

Automating Outlier Detection via Meta-Learning

Zhao¹,

Rossi²,

Akoglu³

2020

Preprint

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Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)? Thus far, model selection for OD has been a "black art"; as any model evaluation is infeasible due to the lack of (i) hold-out data with labels, and (ii) a universal objective function. In this work, we develop the first principled datadriven approach to model selection for OD, called METAOD, based on meta-learning. METAOD capitalizes on the past performances of a large body of detection models on existing outlier detection benchmark datasets, and carries over this prior experience to automatically select an effective model to be employed on a new dataset. To capture task similarity, we introduce specialized meta-features that quantify outlying characteristics of a dataset. Through comprehensive experiments, we show the effectiveness of METAOD in selecting a detection model that significantly outperforms the most popular outlier detectors (e.g., LOF and iForest) as well as various state-of-the-art unsupervised meta-learners while being extremely fast. To foster reproducibility and further research on this new problem, we open-source our entire meta-learning system, benchmark environment, and testbed datasets.

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“…The natural next step is to construct a method that selects the heuristic or set of heuristics to use for a given instance based on that instance's characteristics, termed an offline learning heuristic selection hyper-heuristic (Burke et al 2010) or an algorithm portfolio. This is also referred to in the literature as the algorithm selection problem, and has been used to create solvers for satisfiability testing (Xu et al 2008, Nikolić et al 2013, quantified Boolean formulas (Pulina and Tacchella 2009), constraint programming (O'Mahony et al 2008), and planning problems (Howe et al 2000). Unlike many hyper-heuristics found in the literature, our procedure does not change the heuristic used during the solution process, nor does it construct a heuristic by selecting from a set of heuristic components.…”

Section: Algorithm Portfoliomentioning

confidence: 99%

What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO

Dunning

Gupta

Silberholz

2018

INFORMS Journal on Computing

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Though empirical testing is broadly used to evaluate heuristics, there are shortcomings with how it is often applied in practice. In a systematic review of Max-Cut and Quadratic Unconstrained Binary Optimization (QUBO) heuristics papers, we found only 4% publish source code, only 14% compare heuristics with identical termination criteria, and most experiments are performed with an artificial, homogeneous set of problem instances. To address these limitations, we implement and release as open-source a code-base of 10 Max-Cut and 27 QUBO heuristics. We perform heuristic evaluation using cloud computing on a library of 3,296 instances. This large-scale evaluation provides insight into the types of problem instances for which each heuristic performs well or poorly. Because no single heuristic outperforms all others across all problem instances, we use machine learning to predict which heuristic will work best on a previously unseen problem instance, a key question facing practitioners.

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Simple algorithm portfolio for SAT

Cited by 21 publications

References 14 publications

MedleySolver: Online SMT Algorithm Selection

MedleySolver: Online SMT Algorithm Selection

Automating Outlier Detection via Meta-Learning

What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO

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