# Computing tight bounds via piecewise linear functions through the example of circle cutting problems

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## Abstract

This paper discusses approximations of continuous and mixed-integer non-linear optimization problems via piecewise linear functions. Various variants of circle cutting problems are considered, where the non-overlap of circles impose a non-convex feasible region. While the paper is written in an “easy-to-understand” and “hands-on” style which should be accessible to graduate students, also new ideas are presented. Specifically, piecewise linear functions are employed to yield mixed-integer linear programming problems which provide lower and upper bounds on the original problem, the circle cutting problem. The piecewise linear functions are modeled by five different formulations, containing the incremental and logarithmic formulations. Another variant of the cutting problem involves the assignment of circles to pre-defined rectangles. We introduce a new global optimization algorithm, based on piecewise linear function approximations, which converges in finitely many iterations to a globally optimal solution. The discussed formulations are implemented in GAMS. All GAMS-files are available for download in the Electronic supplementary material. Extensive computational results are presented with various illustrations.

## Keywords

Piecewise linear functions Circle cutting Non-convex optimization Global optimization Non-linear programming (NLP) Quadratically constrained programming (QCP) Mixed-integer linear programming (MILP) Outer approximation Inner approximation Incremental formulation Logarithmic formulation## Notes

### Acknowledgments

The author thanks Ed Klotz (IBM CPLEX) and two anonymous reviewers for their valuable suggestions.

## Supplementary material

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