Global and Superlinear Convergence of a Class of Variable Metric Methods.
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WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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This paper considers a class of variable metric methods for unconstrained minimization. Without requiring exact line searches it is shown that, under appropriate assumptions on the function to be minimized, each algorithm in this class converges globally and superlinearly. Author
- Operations Research