Data networks
An outer-approximation algorithm for a class of mixed-integer nonlinear programs
Mathematical Programming: Series A and B
Generalized Convex Disjunctive Programming: Nonlinear Convex Hull Relaxation
Computational Optimization and Applications
Mathematical Programming: Series A and B
Perspective cuts for a class of convex 0–1 mixed integer programs
Mathematical Programming: Series A and B
Perspective relaxation of mixed integer nonlinear programs with indicator variables
IPCO'08 Proceedings of the 13th international conference on Integer programming and combinatorial optimization
An algorithmic framework for convex mixed integer nonlinear programs
Discrete Optimization
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In this paper, we study MINLPs featuring "on/off" constraints. An "on/off" constraint is a constraint f(x)驴0 that is activated whenever a corresponding 0---1 variable is equal to 1. Our main result is an explicit characterization of the convex hull of the feasible region when the MINLP consists of simple bounds on the variables and one "on/off" constraint defined by an isotone function f. When extended to general convex MINLPs, we show that this result yields tight lower bounds compared to classical formulations. This allows us to introduce new models for the delay-constrained routing problem in telecommunications. Numerical results show gains in computing time of up to one order of magnitude compared to state-of-the-art approaches.