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Some transformation techniques with applications in global optimization

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Abstract

In this paper some transformation techniques, based on power transformations, are discussed. The techniques can be applied to solve optimization problems including signomial functions to global optimality. Signomial terms can always be convexified and underestimated using power transformations on the individual variables in the terms. However, often not all variables need to be transformed. A method for minimizing the number of original variables involved in the transformations is, therefore, presented. In order to illustrate how the given method can be integrated into the transformation framework, some mixed integer optimization problems including signomial functions are finally solved to global optimality using the given techniques.

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References

  1. Beale E.M.L. and Forrest J.J.H. (1976). Global optimization using special ordered sets. Math. Program. 10: 52–69

    Article  Google Scholar 

  2. Björk, K.-M.: A global optimization method with some heat exchanger network applications, Ph.D. thesis, Åbo Akademi University (2002)

  3. Björk K.-M., Lindberg P.O. and Westerlund T. (2003). Some convexifications in global optimization of problems containing signomial terms. Comp. Chem. Eng. 27: 669–679

    Article  Google Scholar 

  4. Lundell, A.: Optimization techniques in global optimization. Master’s thesis, Åbo Akademi University (2007)

  5. Maranas C.D. and Floudas C.A. (1995). Finding all solutions of nonlinearly constrained systems of equations. J. Global Optim. 7: 143–182

    Article  Google Scholar 

  6. Maranas C.D. and Floudas C.A. (1997). Global optimization in generalized geometric programming. Comp. Chem. Eng. 21: 351–370

    Article  Google Scholar 

  7. Pörn, R.: Mixed integer non-linear programming: convexification techniques and algorithm development, Ph.D. thesis, Åbo Akademi University (2000)

  8. Rijckaert M.J. and Martens X.M. (1978). Comparison of generalized geometric programming algorithms. J. Optim.Theory Appl. 26(2): 205–242

    Article  Google Scholar 

  9. Westerlund, T.: Some transformation techniques in global optimization. Global Optimization: From Theory to Implementation. In: Liberti, L., Maculan, N. (eds.), pp. 47–74 Springer (2005)

  10. Westerlund T. and Pörn R. (2002). Solving pseudo-convex mixed-integer problems by cutting plane techniques. Optim. Eng. 3: 253–280

    Article  Google Scholar 

  11. Westerlund T. and Westerlund J. (2003). GGPECP—an algorithm for solving non-convex MINLP problems by cutting plane and transformation techniques. Chem. Eng. Trans. 3: 1045–1050

    Google Scholar 

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Correspondence to Tapio Westerlund.

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Lundell, A., Westerlund, J. & Westerlund, T. Some transformation techniques with applications in global optimization. J Glob Optim 43, 391–405 (2009). https://doi.org/10.1007/s10898-007-9223-4

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  • DOI: https://doi.org/10.1007/s10898-007-9223-4

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