A superlinearly convergent algorithm for min-max problems

Algorithms for solving the problem of minimizing the maximum of a finite number of functions are proposed and analyzed. Quadratic approximations to the functions are used in determination of a search direction. Global convergence is proved, and it is shown that the rate of convergence is quadratic in the convex case and superlinear in the nonconvex case.<<ETX>>

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