Sequence of Polyhedral Relaxations for Nonlinear Univariate Functions

@article{Sundar2020SequenceOP,
  title={Sequence of Polyhedral Relaxations for Nonlinear Univariate Functions},
  author={K. Sundar and S. Sanjeevi and H. Nagarajan},
  journal={arXiv: Optimization and Control},
  year={2020}
}
The letter develops a sequence of Mixed Integer Linear Programming (MILP) and Linear Programming (LP) relaxations that converge to the graph of a nonlinear, univariate, bounded, and differentiable function $f(x)$ and its convex hull, respectively. Theoretical convergence of the sequence of relaxations to the graph of the function and its convex hull is established. These relaxations can be used in MILP-based global optimization algorithms for nonlinear non-convex optimization problems. 

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