A Superlinearly Convergent Method for Minimization Problems with Linear Inequality Constraints.
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WISCONSIN UNIV MADISON MATHEMATICS RESEARCH CENTER
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A method is described for minimizing a continuously differentiable function Fx of n variables subject to linear inequality constraints. It can be applied under the same general assumptions as any method of feasible directions. If Fx is twice continuously differentiable and the Hessian matrix of Fx has certain properties, then the algorithm generates a sequence of points which converges superlinearly to the unique minimizer of Fx. No computation of second order derivatives is required. Author
- Operations Research