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Nonconvex Global Optimization of the Separable Resource Allocation Problem with Continuous Variables

  • Emile Haddad
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 7)

Abstract

New results are presented for solving the well-known nonlinear programming problem: Minimize F= ∑ fi(xi) subject to ∑ xi=r and xi ≥ 0; which has been studied over the past thirty years in numerous application areas. Whereas current solution methods are restricted to convex fi(xi), the new results allow the functions fi(xi) to be nonconvex and multimodal with any number of maxima and minima over [0, X]. Necessary conditions for the local minima of F(x1, x2,… xn) are derived. The necessary conditions for local minima are used to determine a superset M of the set of all local minima. Minimization of the objective function F over M leads to the global minimum. The results are used to solve examples which no other analytical criteria can solve.

Keywords

Global Optimization Minimum Point Feasible Point Resource Allocation Problem Derivative Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Emile Haddad
    • 1
  1. 1.Computer Science DepartmentVirginia TechFalls ChurchUSA

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