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Continuous Piecewise Linear Delta-Approximations for Univariate Functions: Computing Minimal Breakpoint Systems

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  • Steffen Rebennack

    (Colorado School of Mines)

  • Josef Kallrath

    (University of Florida
    Scientific Computing)

Abstract

For univariate functions, we compute optimal breakpoint systems subject to the condition that the piecewise linear approximator, under-, and over-estimator never deviate more than a given $$\delta $$ δ -tolerance from the original function over a given finite interval. The linear approximators, under-, and over-estimators involve shift variables at the breakpoints allowing for the computation of an optimal piecewise linear, continuous approximator, under-, and over-estimator. We develop three non-convex optimization models: two yield the minimal number of breakpoints, and another in which, for a fixed number of breakpoints, the breakpoints are placed such that the maximal deviation is minimized. Alternatively, we use two heuristics which compute the breakpoints subsequently, solving small non-convex problems. We present computational results for 10 univariate functions. Our approach computes breakpoint systems with up to one order of magnitude less breakpoints compared to an equidistant approach.

Suggested Citation

  • Steffen Rebennack & Josef Kallrath, 2015. "Continuous Piecewise Linear Delta-Approximations for Univariate Functions: Computing Minimal Breakpoint Systems," Journal of Optimization Theory and Applications, Springer, vol. 167(2), pages 617-643, November.
    • Handle: RePEc:spr:joptap:v:167:y:2015:i:2:d:10.1007_s10957-014-0687-3
    DOI: 10.1007/s10957-014-0687-3
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    References listed on IDEAS

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    1. Lopez, Marco & Still, Georg, 2007. "Semi-infinite programming," European Journal of Operational Research, Elsevier, vol. 180(2), pages 491-518, July.
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    Cited by:

    1. Aakil M. Caunhye & Douglas Alem, 2023. "Practicable robust stochastic optimization under divergence measures with an application to equitable humanitarian response planning," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(3), pages 759-806, September.
    2. Corina Birghila & Tim J. Boonen & Mario Ghossoub, 2020. "Optimal Insurance under Maxmin Expected Utility," Papers 2010.07383, arXiv.org.
    3. Aloïs Duguet & Christian Artigues & Laurent Houssin & Sandra Ulrich Ngueveu, 2022. "Properties, Extensions and Application of Piecewise Linearization for Euclidean Norm Optimization in $$\mathbb {R}^2$$ R 2," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 418-448, November.
    4. Steffen Rebennack, 2022. "Data-driven stochastic optimization for distributional ambiguity with integrated confidence region," Journal of Global Optimization, Springer, vol. 84(2), pages 255-293, October.
    5. Andreas Bärmann & Robert Burlacu & Lukas Hager & Thomas Kleinert, 2023. "On piecewise linear approximations of bilinear terms: structural comparison of univariate and bivariate mixed-integer programming formulations," Journal of Global Optimization, Springer, vol. 85(4), pages 789-819, April.
    6. Kevin McCoy & Vitaliy Krasko & Paul Santi & Daniel Kaffine & Steffen Rebennack, 2016. "Minimizing economic impacts from post-fire debris flows in the western United States," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 149-176, August.
    7. Ngueveu, Sandra Ulrich, 2019. "Piecewise linear bounding of univariate nonlinear functions and resulting mixed integer linear programming-based solution methods," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1058-1071.
    8. Nathan Sudermann-Merx & Steffen Rebennack, 2021. "Leveraged least trimmed absolute deviations," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 809-834, September.
    9. Noam Goldberg & Steffen Rebennack & Youngdae Kim & Vitaliy Krasko & Sven Leyffer, 2021. "MINLP formulations for continuous piecewise linear function fitting," Computational Optimization and Applications, Springer, vol. 79(1), pages 223-233, May.
    10. Steffen Rebennack & Vitaliy Krasko, 2020. "Piecewise Linear Function Fitting via Mixed-Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 507-530, April.
    11. López-Ramos, Francisco & Nasini, Stefano & Sayed, Mohamed H., 2020. "An integrated planning model in centralized power systems," European Journal of Operational Research, Elsevier, vol. 287(1), pages 361-377.

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