PAVER

PAVER 2.0: an open source environment for automated performance analysis of benchmarking data. In this paper we describe PAVER 2.0, an environment (i.e. a process and a suite of tools supporting that process) for the automated performance analysis of benchmarking data. This new environment improves on its predecessor by addressing some of the shortcomings of the original PAVER (Bussieck et al. in Global optimization and constraint satisfaction, lecture notes in computer science, vol 2861, pp 223--238. Springer, Berlin, 2003; url{doi:10.1007/978-3-540-39901-8_17}) and extending its capabilities. The changes serve to further the original goals of PAVER (automation of the visualization and summarization of benchmarking data) while making the environment more accessible for the use of and modification by the entire community of potential users. In particular, we have targeted the end-users of optimization software, as they are best able to make the many subjective choices necessary to produce impactful results when benchmarking optimization software. We illustrate with some sample analyses conducted via PAVER 2.0.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)
  2. Anders Markvardsen, Tyrone Rees, Michael Wathen, Andrew Lister, Patrick Odagiu, Atijit Anuchitanukul, Tom Farmer, Anthony Lim, Federico Montesino, Tim Snow, Andrew McCluskey: FitBenchmarking: an open source Python package comparing data fitting software (2021) not zbMATH
  3. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; TuĊĦar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  4. Bernal, David E.; Vigerske, Stefan; Trespalacios, Francisco; Grossmann, Ignacio E.: Improving the performance of DICOPT in convex MINLP problems using a feasibility pump (2020)
  5. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  6. Lima, Ricardo M.; Grossmann, Ignacio E.: On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study (2017)
  7. Kronqvist, Jan; Lundell, Andreas; Westerlund, Tapio: The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming (2016)
  8. Bussieck, Michael R.; Dirkse, Steven P.; Vigerske, Stefan: PAVER 2.0: an open source environment for automated performance analysis of benchmarking data (2014)
  9. Mittelmann, Hans D.; Pruessner, Armin: A server for automated performance analysis of benchmarking data (2006)
  10. Bussieck, Michael R.; Drud, Arne Stolbjerg; Meeraus, Alexander; Pruessner, Armin: Quality assurance and global optimization (2003)