Optimization Online


Non-Convex Mixed-Integer Nonlinear Programming: A Survey

Samuel Burer (samuel-burer***at***uiowa.edu)
Adam Letchford (a.n.letchford***at***lancaster.ac.uk)

Abstract: A wide range of problems arising in practical applications can be formulated as Mixed-Integer Nonlinear Programs (MINLPs). For the case in which the objective and constraint functions are convex, some quite effective exact and heuristic algorithms are available. When non-convexities are present, however, things become much more difficult, since then even the continuous relaxation is a global optimisation problem. We survey the literature on non-convex MINLP, discussing applications, algorithms and software. Special attention is paid to the case in which the objective and constraint functions are quadratic.

Keywords: mixed-integer nonlinear programming, global optimisation, quadratic programming, polynomial optimisation mixed-integer nonlinear programming, global optimi- sation, quadratic programming, polynomial optimisation

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Global Optimization

Category 3: Integer Programming ((Mixed) Integer Linear Programming )

Citation: S. Burer & A.N. Letchford (2012) Non-convex mixed-integer nonlinear programming: a survey. Surveys in Oper. Res. and Mgmt. Sci., 17, 97-106.


Entry Submitted: 02/29/2012
Entry Accepted: 02/29/2012
Entry Last Modified: 09/05/2012

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society