OSiL

OSiL: An instance language for optimization. Distributed computing technologies such as Web Services are growing rapidly in importance in today’s computing environment. In the area of mathematical optimization, it is common to separate modeling languages from optimization solvers. In a completely distributed environment, the modeling language software, solver software, and data used to generate a model instance might reside on different machines using different operating systems. Such a distributed environment makes it critical to have an open standard for exchanging model instances.par In this paper we present OSiL (Optimization Services instance Language), an XML-based computer language for representing instances of large-scale optimization problems including linear programs, mixed-integer programs, quadratic programs, and very general nonlinear programs. OSiL has two key features that make it much superior to current standard forms for optimization problem instances. First, it uses the object-oriented features of XML schemas to efficiently represent nonlinear expressions. Second, its XML schema maps directly into a corresponding in-memory representation of a problem instance. The in-memory representation provides a robust application program interface for general nonlinear programming, facilitates reading and writing postfix, prefix, and infix formats to and from the nonlinear expression tree, and makes the expression tree readily available for function and derivative evaluations.


References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Legat, Benoît; Dowson, Oscar; Garcia, Joaquim Dias; Lubin, Miles: MathOptInterface: a data structure for mathematical optimization problems (2022)
  2. Ceccon, Francesco; Siirola, John D.; Misener, Ruth: SUSPECT: MINLP special structure detector for Pyomo (2020)
  3. Andrea Callia D’Iddio, Michael Huth: Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving (2017) arXiv
  4. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  5. Kronqvist, Jan; Lundell, Andreas; Westerlund, Tapio: The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming (2016)
  6. Cailloux, Olivier; Tervonen, Tommi; Verhaegen, Boris; Picalausa, François: A data model for algorithmic multiple criteria decision analysis (2014)
  7. Watson, Jean-Paul; Woodruff, David L.; Hart, William E.: PySP: modeling and solving stochastic programs in Python (2012)
  8. Fourer, Robert; Ma, Jun; Martin, Kipp: OSiL: An instance language for optimization (2010)
  9. Fourer, R.; Gassmann, H. I.; Ma, J.; Martin, R. K.: An XML-based schema for stochastic programs (2009) ioport
  10. Fourer, R.; Gassmann, H. I.; Ma, J.; Martin, R. K.: An XML-based schema for stochastic programs (2009)
  11. Fourer, Robert; Lopes, Leo; Martin, Kipp: LPFML: A W3C XML schema for linear and integer programming (2005)


Further publications can be found at: http://www.coin-or.org/OS/publications_papers.html