A Comprehensive Study of k-Portfolios of Recent SAT Solvers

These are the slides for the paper "A Comprehensive Study of k-Portfolios of Recent SAT Solvers", presented at the conference [*SAT 2022*](http://satisfiability.org/SAT22/). You can find the paper [here](https://www.doi.org/10.4230/LIPIcs.SAT.2022.2)

Finding Optimal Diverse Feature Sets with Alternative Feature Selection

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods typically yield one feature set only, which might not suffice in some scenarios. For example, users might be interested in finding alternative feature sets with similar prediction quality, offering different explanations of the data. In this article, we introduce alternative feature selection and formalize it as an optimization problem. In particular, we define alternatives via constraints and enable users to control the number and dissimilarity of alternatives. Next, we analyze the complexity of this optimization problem and show NP-hardness. Further, we discuss how to integrate conventional feature-selection methods as objectives. Finally, we evaluate alternative feature selection with 30 classification datasets. We observe that alternative feature sets may indeed have high prediction quality, and we analyze several factors influencing this outcome

Finding Optimal Diverse Feature Sets with Alternative Feature Selection

Feature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods typically yield one feature set only, which might not suffice in some scenarios. For example, users might be interested in finding alternative feature sets with similar prediction quality, offering different explanations of the data. In this article, we introduce alternative feature selection and formalize it as an optimization problem. In particular, we define alternatives via constraints and enable users to control the number and dissimilarity of alternatives. Next, we analyze the complexity of this optimization problem and show NP-hardness. Further, we discuss how to integrate conventional feature-selection methods as objectives. Finally, we evaluate alternative feature selection with 30 classification datasets. We observe that alternative feature sets may indeed have high prediction quality, and we analyze several factors influencing this outcome

A Comprehensive Study of k-Portfolios of Recent SAT Solvers

Hard combinatorial problems such as propositional satisfiability are ubiquitous. The holy grail are solution methods that show good performance on all problem instances. However, new approaches emerge regularly, some of which are complementary to existing solvers in that they only run faster on some instances but not on many others. While portfolios, i.e., sets of solvers, have been touted as useful, putting together such portfolios also needs to be efficient. In particular, it remains an open question how well portfolios can exploit the complementarity of solvers. This paper features a comprehensive analysis of portfolios of recent SAT solvers, the ones from the SAT Competitions 2020 and 2021. We determine optimal portfolios with exact and approximate approaches and study the impact of portfolio size k on performance. We also investigate how effective off-the-shelf prediction models are for instance-specific solver recommendations. One result is that the portfolios found with an approximate approach are as good as the optimal solution in practice. We also observe that marginal returns decrease very quickly with larger k, and our prediction models do not give way to better performance beyond very small portfolio sizes

Active Learning for SAT Solver Benchmarking

Benchmarking is a crucial phase when developing algorithms. This also applies to solvers for the SAT (propositional satisfiability) problem. Benchmark selection is about choosing representative problem instances that reliably discriminate solvers based on their runtime. In this paper, we present a dynamic benchmark selection approach based on active learning. Our approach predicts the rank of a new solver among its competitors with minimum runtime and maximum rank prediction accuracy. We evaluated this approach on the Anniversary Track dataset from the 2022 SAT Competition. Our selection approach can predict the rank of a new solver after about 10 % of the time it would take to run the solver on all instances of this dataset, with a prediction accuracy of about 92 %. We also discuss the importance of instance families in the selection process. Overall, our tool provides a reliable way for solver engineers to determine a new solver’s performance efficiently

Data-driven exploration and continuum modeling of dislocation networks

The microstructural origin of strain hardening during plastic deformation in stage II deformation of face-centered cubic (fcc) metals can be attributed to the increase in dislocation density resulting in a formation of dislocation networks. Although this is a well known relation, the complexity of dislocation multiplication processes and details about the formation of dislocation networks have recently been revealed by discrete dislocation dynamics (DDD) simulations. It has been observed that dislocations, after being generated by multiplication mechanisms, show a limited expansion within their slip plane before they get trapped in the network by dislocation reactions. This mechanism involves multiple slip systems and results in a heterogeneous dislocation network, which is not reflected in most dislocation-based continuum models. We approach the continuum modeling of dislocation networks by using data science methods to provide a link between discrete dislocations and the continuum level. For this purpose, we identify relevant correlations that feed into a model for dislocation networks in a dislocation-based continuum theory of plasticity. As a key feature, the model combines the dislocation multiplication with the limitation of the travel distance of dislocations by formation of stable dislocation junctions. The effective mobility of the network is determined by a range of dislocation spacings which reproduces the scattering travel distances of generated dislocation as observed in DDD. The model is applied to a high-symmetry fcc loading case and compared to DDD simulations. The results show a physically meaningful microstructural evolution, where the generation of new dislocations by multiplication mechanisms is counteracted by a formation of a stable dislocation network. In conjunction with DDD, we observe a steady state interplay of the different mechanisms

An Empirical Evaluation of Constrained Feature Selection

While feature selection helps to get smaller and more understandable prediction models, most existing feature-selection techniques do not consider domain knowledge. One way to use domain knowledge is via constraints on sets of selected features. However, the impact of constraints, e.g., on the predictive quality of selected features, is currently unclear. This article is an empirical study that evaluates the impact of propositional and arithmetic constraints on filter feature selection. First, we systematically generate constraints from various types, using datasets from different domains. As expected, constraints tend to decrease the predictive quality of feature sets, but this effect is non-linear. So we observe feature sets both adhering to constraints and with high predictive quality. Second, we study a concrete setting in materials science. This part of our study sheds light on how one can analyze scientific hypotheses with the help of constraints

The 2020 Evaluation of the Finnish Environment Institute, SYKE

The report presents a summary of the international evaluation of the Finnish Environment Institute SYKE and its results. The report was drawn up by the independent evaluation group appointed by the Ministry of the Environment in May 2020. Its task was to evaluate the relevance and quality of the activities of SYKE especially from the following perspectives: 1) quality and impact of the expert services, 2) societal impact and sustainability leadership, 3) cooperation and role in networks and 4) foresight and innovation. The findings and recommendations of the evaluation group are based on the quantitative and qualitative background materials provided to the group and interviews with leaders and key researchers of SYKE, representatives of the Ministry of the Environment selected by the Ministry, and stakeholder representatives. The evaluation group considers that SYKE is a progressive research institute that is widely appreciated in society. It produces research and expertise of a high standard and its societal impact is significant. The evaluators identified possibilities for development in making an impact in both national and international contexts, where SYKE could show even stronger societal leadership as a promoter of sustainable development. The group proposes numerous recommendations relating to this. The evaluation was conducted during June-September 2020