Abstract
The convergence rate is analyzed for McCormick relaxations of compositions of the form \(F \circ f\), where F is a multivariate function, as established by Tsoukalas and Mitsos (J Glob Optim 59:633–662, 2014). Convergence order in the Hausdorff metric and pointwise convergence order are analyzed. Similar to the convergence order propagation of McCormick univariate composition functions, Bompadre and Mitsos (J Glob Optim 52(1):1–28, 2012), the convergence order of the multivariate composition is determined by the minimum of the orders of the inclusion functions of the inner functions and the convergence order of the multivariate outer function. The convergence order in the Hausdorff metric additionally depends on the enclosure order of the image of the inner functions introduced in this work. The result established holds for any composition and can be further specialized for specific compositions. In some cases this specialization results in the bounds established by Bompadre and Mitsos. Examples of important functions, e.g., binary product of functions and minimum of functions show that the convergence rate of the relaxations based on multivariate composition theorem results in a higher convergence rate than the convergence rate of univariate McCormick relaxations. Refined bounds, employing also the range order, similar to those determined by Bompadre et al. (J Glob Optim 57(1):75–114, 2013), on the convergence order of McCormick relaxations of univariate and multivariate composite functions are developed.
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References
Adjiman, C.S., Androulakis, I.P., Floudas, C.A.: A global optimization method, \(\alpha \)BB, for general twice-differentiable constrained NLPs-II. Implementation and computational results. Comput. Chem. Eng. 22(9), 1159–1179 (1998)
Adjiman, C.S., Floudas, C.A.: Rigorous convex underestimators for general twice-differentiable problems. J. Glob. Optim. 9(1), 23–40 (1996)
Alefeld, G., Mayer, G.: Interval analysis: theory and applications. J. Comput. Appl. Math. 121(1–2), 421–464 (2000)
Alt, H., Bra, P., Godau, M., Knauer, C., Wenk, C.: Computing the Hausdorff Distance of Geometric Patterns and Shapes, Algorithms and Combinatorics, vol. 25. Springer, Berlin (2003)
Alt, H., Scharf, L.: Computing the Hausdorff distance between curved objects. Int. J. Comput. Geom. Appl. 18(04), 307–320 (2008)
Atallah, M.J.: A linear time algorithm for the Hausdorff distance between convex polygons. Inf. Process. Lett. 17(4), 207–209 (1983)
Bompadre, A., Mitsos, A.: Convergence rate of McCormick relaxations. J. Glob. Optim. 52(1), 1–28 (2012)
Bompadre, A., Mitsos, A., Chachuat, B.: Convergence analysis of Taylor models and McCormick-Taylor models. J. Glob. Optim. 57(1), 75–114 (2013)
Du, K., Kearfott, R.B.: The cluster problem in multivariate global optimization. J. Glob. Optim. 5(3), 253–265 (1994)
Forster, O.: Analysis 2 Differentialrechnung im \({\mathbb{R}}^n\), gewöhnliche Differentialgleichungen. Springer Spektrum, Heidelberg (2013)
Horst, R., Tuy, H.: Global Optimization: Deterministic Approaches. Springer Science & Business Media, Berlin (1996)
Kurzhanski, A.B., Varaiya, P.: Ellipsoidal techniques for reachability analysis: internal approximation. Syst. Control Lett. 41(3), 201–211 (2000)
Kurzhanski, A.B., Varaiya, P.: Reachability analysis for uncertain systems—the ellipsoidal technique. DCDIS Ser. B 9, 347–368 (2002)
Maranas, C.D., Floudas, C.A.: A global optimization approach for Lennard–Jones microclusters. J. Chem. Phys. 97(10), 7667–7678 (1992)
Maranas, C.D., Floudas, C.A.: Finding all solutions of nonlinearly constrained systems of equations. J. Glob. Optim. 7(2), 143–182 (1995)
McCormick, G.P.: Computability of global solutions to factorable nonconvex programs: part I. Convex underestimating problems. Math. Progr. 10, 147–175 (1976)
McCormick, G.P.: Nonlinear Programming: Theory, Algorithms, and Applications. Wiley, New York (1983)
Mitsos, A., Chachuat, B., Barton, P.I.: McCormick-based relaxations of algorithms. SIAM J. Optim. 20(2), 573–601 (2009)
Moore, R.E., Bierbaum, F.: Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics). Society for Industrial & Applied Mathematics, Philadelphia (1979)
Munkres, J.: Topology. Prentice Hall, Englewood Cliffs (1999)
Neumaier, A.: Complete search in continuous global optimization and constraint satisfaction. Acta Numer. 13, 271–369 (2004)
O’Searcoid, M.: Metric Spaces. Springer Undergraduate Mathematics Series. Springer, London (2006)
Ratschek, H., Rokne, J.: Computer Methods for the Range of Functions. E. Horwood, New York (1984)
Rucklidge, W.J.: Efficiently locating objects using the Hausdorff distance. Int. J. Comput. Vis. 24(3), 251–270 (1997)
Schöbel, A., Scholz, D.: The theoretical and empirical rate of convergence for geometric branch-and-bound methods. J. Glob. Optim. 48(3), 473–495 (2010)
Scott, J.K., Stuber, M.D., Barton, P.I.: Generalized McCormick relaxations. J. Glob. Optim. 51(4), 569–606 (2011)
Smith, E.M., Pantelides, C.C.: Global optimisation of nonconvex minlps. Comput. Chem. Eng. 21, 791–796 (1997)
Tawarmalani, M., Sahinidis, N.V.: Semidefinite relaxations of fractional programs via novel convexification techniques. J. Glob. Optim. 20(2), 133–154 (2001)
Tawarmalani, M., Sahinidis, N.V.: Convex extensions and envelopes of lower semi-continuous functions. Math. Progr. 93, 247–263 (2002)
Tawarmalani, M., Sahinidis, N.V.: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications, vol. 65. Springer Science & Business Media, Berlin (2002)
Tawarmalani, M., Sahinidis, N.V.: A polyhedral branch-and-cut approach to global optimization. Math. Progr. 103(2), 225–249 (2005)
Tsoukalas, A., Mitsos, A.: Multivariate McCormick relaxations. J. Glob. Optim. 59, 633–662 (2014)
Wechsung, A., Barton, P.I.: Global optimization of bounded factorable functions with discontinuities. J. Glob. Optim. 58(1), 1–30 (2014)
Wechsung, A., Schaber, S.D., Barton, P.I.: The cluster problem revisited. J. Glob Optim. 58(3), 429–438 (2014)
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Appendix
Appendix
Proof of the equivalence of Definition 10 and the definition used in [7, 8, 23]:
Definition
(Hausdorff convergence) Let \(Z \subset \mathbb {R}^n\) and \(f:Z \rightarrow \mathbb {R}\) be a continuous function, and let \(H_f\) be an inclusion function of f on Z. The inclusion function \(H_f\) has Hausdorff convergence of order \(\beta >0\) if there exists a constant \(\tau >0\) such that, for any interval \(Y \in \mathbb {I}Z\),
Proof
Let \(Z\subset \mathbb {R}^n\), \(f:Z\rightarrow \mathbb {R}\), \(H_f\) be its inclusion function, \(Y\in \mathbb {I}Z\) and \(\tau ,\beta >0\) be given as in Definition above with
Since \(\bar{f}(Y) \subset H_f(Y)\), the equivalence directly follows from
\(\square \)
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Najman, J., Mitsos, A. Convergence analysis of multivariate McCormick relaxations. J Glob Optim 66, 597–628 (2016). https://doi.org/10.1007/s10898-016-0408-6
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DOI: https://doi.org/10.1007/s10898-016-0408-6