FilMINT: An Outer Approximation-Based Solver for Convex Mixed-Integer Nonlinear Programs

Published Online:https://doi.org/10.1287/ijoc.1090.0373

References

  • Achterberg T. Conflict analysis in mixed-integer programming. Discrete Optim. (2007) 4(1):4–20CrossrefGoogle Scholar
  • Atamtürk A., Savelsbergh M. W. P. Integer-programming software systems. Ann. Oper. Res. (2005) 140(1):67–124CrossrefGoogle Scholar
  • Belotti P., Bonami P., Biegler L. T., Conn A. R., Cornuéjols G., Grossmann I. E., Laird C. D., et al. CMU-IBM open source MINLP project. (2006) . Retrieved August 2006, http://egon.cheme.cmu.edu/ibm/page.htmGoogle Scholar
  • Bonami P., Gonçalves J. Primal heuristics for mixed-integer nonlinear programs. (2008) . Technical Report RC24639 (W0809-056), IBM Research Division, Yorktown Heights, NYGoogle Scholar
  • Bonami P., Cornuéjols G., Lodi A., Margot F. A feasibility pump for mixed-integer nonlinear programs. Math. Programming (2008a) 119(2):331–352CrossrefGoogle Scholar
  • Bonami P., Biegler L. T., Conn A. R., Cornuéjols G., Grossmann I. E., Laird C. D., Lee J., et al. An algorithmic framework for convex mixed-integer nonlinear programs. Discrete Optim. (2008b) 5(2):186–204CrossrefGoogle Scholar
  • Bussieck M. R., Drud A. S., Meeraus A. MINLPLib—A collection of test models for mixed-integer nonlinear programming. INFORMS J. Comput. (2003) 15(1):114–119LinkGoogle Scholar
  • Castillo I., Westerlund J., Emet S., Westerlund T. Optimization of block layout design problems with unequal areas: A comparison of MILP and MINLP optimization methods. Comput. Chemical Engrg. (2005) 30(1):54–69CrossrefGoogle Scholar
  • Dakin R. J. A tree-search algorithm for mixed programming problems. Comput. J. (1965) 8(3):250–255CrossrefGoogle Scholar
  • Dolan E., Moré J. Benchmarking optimization software with performance profiles. Math. Programming (2002) 91(2):201–213CrossrefGoogle Scholar
  • Duran M. A., Grossmann I. E. An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Math. Programming (1986) 36(3):307–339CrossrefGoogle Scholar
  • Fletcher R., Leyffer S. Solving mixed-integer nonlinear programs by outer approximation. Math. Programming (1994) 66(3):327–349CrossrefGoogle Scholar
  • Fletcher R., Leyffer S. Nonlinear programming without a penalty function. Math. Programming (2002) 91(2):239–270CrossrefGoogle Scholar
  • Fletcher R., Leyffer S., Toint P. L. On the global convergence of a Filter-SQP algorithm. SIAM J. Optim. (2002) 13(1):44–59CrossrefGoogle Scholar
  • Forrest J. COIN-OR branch-and-cut MIP solver. (2004) . https://projects.coin-or.org/CbcGoogle Scholar
  • Forrest J. J. H., Hirst J. P. H., Tomlin J. A. Practical solution of large mixed-integer programming problems with UMPIRE. Management Sci. (1974) 20(5):736–773LinkGoogle Scholar
  • Fourer R., Gay D. M., Kernighan B. W.AMPL: A Modeling Language for Mathematical Programming (1993) (Scientific Press, South San Francisco, CA) Google Scholar
  • Gay D. M. Hooking your solver to AMPL. (1997) . Technical Report 97-4-06, Computing Sciences Research Center, Bell Laboratories, Murray Hill, NJGoogle Scholar
  • Geoffrion A. M. Generalized Benders decomposition. J. Optim. Theory Appl. (1972) 10(4):237–260CrossrefGoogle Scholar
  • Grossmann I. E. Review of nonlinear mixed-integer and disjunctive programming techniques. Optim. Engrg. (2002) 3(3):227–252CrossrefGoogle Scholar
  • Gupta O. K., Ravindran A. Branch and bound experiments in convex nonlinear integer programming. Management Sci. (1985) 31(12):1533–1546LinkGoogle Scholar
  • Harjunkoski I., Westerlund T., Pörn R., Skrifvars H. Different transformations for solving non-convex trim-loss problems by MINLP. Eur. J. Oper. Res. (1988) 105(3):594–603CrossrefGoogle Scholar
  • Jain V., Grossmann I. E. Cyclic scheduling of continuous parallel-process units with decaying performance. AIChE J. (1998) 44(7):1623–1636CrossrefGoogle Scholar
  • Kelley J. E. The cutting-plane method for solving convex programs. J. SIAM (1960) 8(4):703–712Google Scholar
  • Kocis G. R., Grossmann I. E. Global optimization of nonconvex mixed-integer nonlinear programming (MINLP) problems in process synthesis. Indust. Engrg. Chemistry Res. (1988) 27(8):1407–1421CrossrefGoogle Scholar
  • Leyffer S. Deterministic methods for mixed-integer nonlinear programming. (1993) . Ph.D. thesis, University of Dundee, Dundee, Scotland, UKGoogle Scholar
  • Leyffer S. User manual for MINLP-BB. (1998) . University of Dundee, Dundee, Scotland, UK. http://www.mcs.anl.gov/∼leyffer/solvers.htmlGoogle Scholar
  • Leyffer S. MacMINLP. (2003) . Retrieved August 2006, http://wiki.mcs.anl.gov/leyffer/index.php/MacMINLPGoogle Scholar
  • Linderoth J. T., Ralphs T. K., Karlof J. K. Noncommercial software for mixed-integer linear programming. Integer Programming: Theory and Practice (2005) (CRC Press, Boca Raton, FL) 253–303CRC Press Operations Research SeriesGoogle Scholar
  • Linderoth J. T., Savelsbergh M. W. P. A computational study of search strategies in mixed-integer programming. INFORMS J. Comput. (1999) 11(2):173–187LinkGoogle Scholar
  • Nemhauser G. L., Savelsbergh M. W. P., Sigismondi G. C. MINTO, a Mixed-INTeger Optimizer. Oper. Res. Lett. (1994) 15(1):47–58CrossrefGoogle Scholar
  • Quesada I., Grossmann I. E. An LP/NLP based branch and bound algorithm for convex MINLP optimization problems. Comput. Chemical Engrg. (1992) 16(10–11):937–947CrossrefGoogle Scholar
  • Quist A. J., van Gemeert R., Hoogenboom J. E., Ílles T., Roos C., Terlaky T. Application of nonlinear optimization to reactor core fuel reloading. Ann. Nuclear Energy (1998) 26(5):423–448CrossrefGoogle Scholar
  • Savelsbergh M. W. P. Preprocessing and probing techniques for mixed-integer programming problems. ORSA J. Comput. (1994) 6(4):445–454LinkGoogle Scholar
  • Stubbs R., Mehrotra S. A branch-and-cut method for 0-1 mixed convex programming. Math. Programming (1999) 86(3):515–532CrossrefGoogle Scholar
  • Wächter A., Biegler L. T. On the implementation of an interior-point filter line search algorithm for large-scale nonlinear programming. Math. Programming (2006) 106(1):25–57CrossrefGoogle Scholar
  • Westerlund T., Pettersson F. An extended cutting plane method for solving convex MINLP problems. Comput. Chemical Engrg. (1995) 19(Supplement 1):131–136CrossrefGoogle Scholar
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