Abstract
Interval Branch-and-Bound (B&B) algorithms are powerful methods which aim for guaranteed solutions of Global Optimisation problems. Lower bounds for a function in a given interval can be obtained directly with Interval Arithmetic. The use of lower bounds based on Taylor forms show a faster convergence to the minimum with decreasing size of the search interval. Our research focuses on one dimensional functions that can be decomposed into several terms (sub-functions). The question is whether using this characteristic leads to sharper bounds when based on bounds of the sub-functions. This paper deals with functions that are an addition of two sub-functions, also called additively separable functions. The use of the separability is investigated for the so-called Baumann form and Lower Bound Value Form (LBVF). It is proven that using the separability in the LBVF form may lead to a combination of linear minorants that are sharper than the original one. Numerical experiments confirm this improving behaviour and also show that not all separable methods do always provide sharper lower bounds.
This work has been funded by grants from the Spanish Ministry of Science and Innovation (TIN2008-01117), and Junta de Andalucía (P11-TIC-7176), in part financed by the European Regional Development Fund (ERDF). Eligius M.T. Hendrix is a fellow of the Spanish “Ramón y Cajal” contract program, co-financed by the European Social Fund.
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References
Baumann, E.: Optimal centered forms. BIT 28(1), 80–87 (1988), doi:10.1007/BF01934696
Casado, L., García, I., Martínez, J., Sergeyev, Y.D.: New interval analysis support functions using gradient information in a global minimization algorithm. Journal of Global Optimization 25(4), 345–362 (2003), doi:10.1023/A:1022512411995
Casado, L., García, I., Sergeyev, Y.: Interval algorithms for finding the minimal root in a set of multiextremal one-dimensional nondifferentiable functions. SIAM Journal on Scientific Computing 24(2), 359–376 (2002), doi:10.1137/S1064827599357590
Hansen, P., Lagouanelle, J.L., Messine, F.: Comparison between baumann and admissible simplex forms in interval analysis. Journal of Global Optimization 37, 215–228 (2007), doi:10.1007/s10898-006-9045-9
Moore, R.: Interval analysis. Prentice-Hall, New Jersey (1966)
Moore, R., Kearfott, R., Cloud, M.: Introduction to Interval analysis. SIAM, Philadelphia (2009)
Neumaier, A.: Interval Methods for Systems of Equations. Cambridge University Press, Cambridge (1990)
Ratz, D.: A nonsmooth global optimization technique using slopes - the one-dimensional case. Journal of Global Optimization 14, 365–393 (1999), doi:10.1023/A:1008391326993
Tóth, B., Casado, L.: Multi-dimensional pruning from baumann point in an interval global optimization algorithm. Journal of Global Optimization 38(2), 215–236 (2007), doi:10.1007/s10898-006-9072-6
Vinkó, T., Lagouanelle, J.L., Csendes, T.: A new inclusion function for optimization: Kite – the one dimensional case. Journal of Global Optimization 30, 435–456 (2004), doi:10.1007/s10898-004-8430-5
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Berenguel, J.L., Casado, L.G., García, I., Hendrix, E.M.T., Messine, F. (2012). On Lower Bounds Using Additively Separable Terms in Interval B&B. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31137-6_9
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DOI: https://doi.org/10.1007/978-3-642-31137-6_9
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