An algorithmic framework for convex mixed integer nonlinear programs

  title={An algorithmic framework for convex mixed integer nonlinear programs},
  author={Pierre Bonami and Lorenz T. Biegler and Andrew R. Conn and G{\'e}rard Cornu{\'e}jols and Ignacio E. Grossmann and Carl D. Laird and Jon Lee and Andrea Lodi and François Margot and Nicolas W. Sawaya and Andreas W{\"a}chter},
  journal={Discrete Optimization},
This paper is motivated by the fact that mixed integer nonlinear programming is an important and difficult area for which there is a need for developing new methods and software for solving large-scale problems. Moreover, both fundamental building blocks, namely mixed integer linear programming and nonlinear programming, have seen considerable and steady progress in recent years. Wishing to exploit expertise in these areas as well as on previous work in mixed integer nonlinear programming, this… CONTINUE READING
Highly Influential
This paper has highly influenced 59 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 531 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 304 extracted citations

531 Citations

Citations per Year
Semantic Scholar estimates that this publication has 531 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 29 references

A generalized disjunctive framework for solving discrete-continuous optimization problems with convex relaxations

  • N. W. Sawaya
  • PhD thesis, Chemical Engineering Department…
  • 2006
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…