SUNNY: a Lazy Portfolio Approach for Constraint Solving

Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo

doi:10.1017/s1471068414000179

Cited by 35 publications

(60 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, passing the bound is not so effective if we just focus on proving optimality. A possible explanation is that communicating an 10 Technically sunny-ori can't outperform any single solver since it does not use any bound communication, and could be therefore be excluded from S. However, we realized that sometimes the behaviour of a solver is not purely deterministic. upper bound can be useful to find a better solution (see Figure 5.11) but ineffective when it comes to prove optimality.…”

Section: Test Resultsmentioning

confidence: 98%

“…Most of the work in Chapter 4 has been published in [7,8,10]. A journal version of [7] is currently under revision.…”

Section: Outlinementioning

confidence: 99%

“…Chapter 6 presents an extended version of [11]. Papers [7,8,10,9,11] are joint works with Dr. Jacopo Mauro and…”

Section: Outlinementioning

confidence: 99%

“…In particular, D may contain both According to the Student's t-test [97], we have also analysed the statistical significance of the results. We considered statistically significant two samples of data 10 having a p-value less or equal than 0.05 (i.e., the probability that the observed difference is due to chance should be less than 5%). We realized that -by fixing the portfolio size-for portfolios of size between 5 and 10 the performances of RF are not statistically significant w.r.t.…”

Section: Percentage Of Solved Instancesmentioning

confidence: 99%

“…For instance, in the example in Figure 5.10 we must include also the values found by the timesplit solver for having a fair comparison (no single solver is indeed able to find the values 98 and 99). 10 …”

Section: Empirical Evaluationmentioning

confidence: 99%

See 4 more Smart Citations

Portfolio approaches in constraint programming

Amadini

2015

Constraints

Self Cite

View full text Add to dashboard Cite

Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of -possibly onaverage slower-algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance.In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core.We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.iii iv

show abstract

Section: Test Resultsmentioning

confidence: 98%

“…Most of the work in Chapter 4 has been published in [7,8,10]. A journal version of [7] is currently under revision.…”

Section: Outlinementioning

confidence: 99%

“…Chapter 6 presents an extended version of [11]. Papers [7,8,10,9,11] are joint works with Dr. Jacopo Mauro and…”

Section: Outlinementioning

confidence: 99%

Section: Percentage Of Solved Instancesmentioning

confidence: 99%

Section: Empirical Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Portfolio approaches in constraint programming

Amadini

2015

Constraints

Self Cite

View full text Add to dashboard Cite

show abstract

Algorithm Selection for Dynamic Symbolic Execution: A Preliminary Study

Amadini

Gange

Schachte

et al. 2021

Logic-Based Program Synthesis and Transformation

Self Cite

View full text Add to dashboard Cite

Given a portfolio of algorithms, the goal of Algorithm Selection (AS) is to select the best algorithm(s) for a new, unseen problem instance. Dynamic Symbolic Execution (DSE) brings together concrete and symbolic execution to maximise the program coverage. DSE uses a constraint solver to solve the path conditions and generate new inputs to explore. In this paper we join these lines of research by introducing a model that combines DSE and AS approaches. The proposed AS/DSE model is a generic and flexible framework enabling the DSE engine to solve the path conditions it collects with a portfolio of different solvers, by exploiting and extending the well-known AS techniques that have been developed over the last decade. In this way, one can increase the coverage and sometimes even outperform the aggregate coverage achievable by running simultaneously all the solvers of the portfolio.

show abstract

MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers

Scott

Niemetz

Preiner

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In this paper, we present MachSMT, an algorithm selection tool for Satisfiability Modulo Theories (SMT) solvers. MachSMT supports the entirety of the SMT-LIB language. It employs machine learning (ML) methods to construct both empirical hardness models (EHMs) and pairwise ranking comparators (PWCs) over state-of-the-art SMT solvers. Given an SMT formula $$\mathcal {I}$$ I as input, MachSMT leverages these learnt models to output a ranking of solvers based on predicted run time on the formula $$\mathcal {I}$$ I . We evaluate MachSMT on the solvers, benchmarks, and data obtained from SMT-COMP 2019 and 2020. We observe MachSMT frequently improves on competition winners, winning $$54$$ 54 divisions outright and up to a $$198.4$$ 198.4 % improvement in PAR-2 score, notably in logics that have broad applications (e.g., BV, LIA, NRA, etc.) in verification, program analysis, and software engineering. The MachSMT tool is designed to be easily tuned and extended to any suitable solver application by users. MachSMT is not a replacement for SMT solvers by any means. Instead, it is a tool that enables users to leverage the collective strength of the diverse set of algorithms implemented as part of these sophisticated solvers.

show abstract

SUNNY: a Lazy Portfolio Approach for Constraint Solving

Cited by 35 publications

References 25 publications

Portfolio approaches in constraint programming

Portfolio approaches in constraint programming

Algorithm Selection for Dynamic Symbolic Execution: A Preliminary Study

MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers

Contact Info

Product

Resources

About