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On the complexity of optimization over the standard simplex

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  • de Klerk, E.
  • den Hertog, D.
  • Elabwabi, G.

Abstract

We review complexity results for minimizing polynomials over the standard simplex and unit hypercube. In addition, we derive new results on the computational complexity of approximating the minimum of some classes of functions (including Lipschitz continuous functions) on the standard simplex. The main tools used in the analysis are Bernstein approximation and Lagrange interpolation on the simplex combined with an earlier result by de Klerk et al. [A PTAS for the minimization of polynomials of fixed degree over the simplex, Theoretical Computer Science 361 (2-3) (2006) 210-225].

Suggested Citation

  • de Klerk, E. & den Hertog, D. & Elabwabi, G., 2008. "On the complexity of optimization over the standard simplex," European Journal of Operational Research, Elsevier, vol. 191(3), pages 773-785, December.
    • Handle: RePEc:eee:ejores:v:191:y:2008:i:3:p:773-785
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    1. Bertsimas, Dimitris & Lauprete, Geoffrey J. & Samarov, Alexander, 2004. "Shortfall as a risk measure: properties, optimization and applications," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1353-1381, April.
    2. A.M. Bagirov & A.M. Rubinov, 2000. "Global Minimization of Increasing Positively Homogeneous Functions over the Unit Simplex," Annals of Operations Research, Springer, vol. 98(1), pages 171-187, December.
    3. de Klerk, E. & Laurent, M. & Parrilo, P., 2006. "A PTAS for the minimization of polynomials of fixed degree over the simplex," Other publications TiSEM 603897c9-179e-43e4-9e83-6, Tilburg University, School of Economics and Management.
    4. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    5. NESTEROV, Yu. & WOLKOWICZ, Henry & YE, Yinyu, 2000. "Semidefinite programming relaxations of nonconvex quadratic optimization," LIDAM Reprints CORE 1471, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. de Klerk, E. & Elfadul, G.E.E. & den Hertog, D., 2006. "Optimization of Univariate Functions on Bounded Intervals by Interpolation and Semidefinite Programming," Discussion Paper 2006-26, Tilburg University, Center for Economic Research.
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    Cited by:

    1. James Chok & Geoffrey M. Vasil, 2023. "Convex optimization over a probability simplex," Papers 2305.09046, arXiv.org.
    2. Immanuel Bomze & Stefan Gollowitzer & E. Yıldırım, 2014. "Rounding on the standard simplex: regular grids for global optimization," Journal of Global Optimization, Springer, vol. 59(2), pages 243-258, July.
    3. Titi, Jihad & Garloff, Jürgen, 2017. "Matrix methods for the simplicial Bernstein representation and for the evaluation of multivariate polynomials," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 246-258.
    4. Tareq Hamadneh & Hassan Al-Zoubi & Saleh Ali Alomari, 2020. "Fast Computation of Polynomial Data Points Over Simplicial Face Values," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-13, March.
    5. Sadek, Lakhlifa & Bataineh, Ahmad Sami & Isik, Osman Rasit & Alaoui, Hamad Talibi & Hashim, Ishak, 2023. "A numerical approach based on Bernstein collocation method: Application to differential Lyapunov and Sylvester matrix equations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 475-488.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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