AN ALGORITHMIC FRAMEWORK FOR MINLP WITH SEPARABLE NON-CONVEXITY

@inproceedings{DAmbrosio2012ANAF,
  title={AN ALGORITHMIC FRAMEWORK FOR MINLP WITH SEPARABLE NON-CONVEXITY},
  author={C. D'Ambrosio and Jon Lee and A. W{\"a}chter},
  year={2012}
}
We present an algorithm for Mixed-Integer Nonlinear Programming (MINLP) problems in which the non-convexity in the objective and constraint functions is manifested as the sum of non-convex univariate functions. We employ a lower bounding convex MINLP relaxation obtained by approximating each non-convex function with a piecewise-convex underestimator that is repeatedly refined. The algorithm is implemented at the level of a modeling language. Favorable numerical results are presented. 
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