
doi: 10.1007/bf01589411
A parallel Newton method is described for the minimization of a twice continuously differentiable uniformly convex function F(x). The algorithm generates a sequence \(\{x_ j\}\) which converges superlinearly to the global minimizer of F(x).
Numerical mathematical programming methods, Numerical methods based on nonlinear programming, Nonlinear programming, parallel Newton method, twice continuously differentiable uniformly convex function, parallel algorithms, superlinear convergence, Newton-type methods
Numerical mathematical programming methods, Numerical methods based on nonlinear programming, Nonlinear programming, parallel Newton method, twice continuously differentiable uniformly convex function, parallel algorithms, superlinear convergence, Newton-type methods
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