Rapidly Convergent Algorithms for Nonsmooth Optimization
Final rept. 16 Jun 1988-30 Sep 1990,
WASHINGTON STATE UNIV PULLMAN
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The research supported by this grant has continued the development of efficient methods for solving optimization problems involving implicity defined functions that are not everywhere differentiable. Progress has been made on extending a rapidly convergent algorithm for the single variable case to the n variable case. A specialization of this research has produced a new two matrix quasi-Newton method for smooth minimization. Also, a new fast method has been developed for the single variable case where only function, and not subderivative, values are available.
- Operations Research