Convex Bilevel Programming

  • Jonathan F. Bard
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 30)


Of the algorithms presented in Chapter 5 for finding global minima of linear bilevel programs, the Kuhn-Tucker approach [B11], the variable elimination method [H1], and the complementarity approach [J4] are the most efficient and robust developed to date. These algorithms can be readily extended to solve the linear-quadratic case where the functions F, G and g are linear, and the function f is strictly convex quadratic.


Search Tree Descent Direction Constraint Qualification Bilevel Program Separation Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Jonathan F. Bard
    • 1
  1. 1.Graduate Program in Operations Research, Department of Mechanical EngineeringThe University of TexasAustinUSA

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