A primal truncated newton algorithm with application to large-scale nonlinear network optimization

We describe a new, convergent, primal-feasible algorithm for linearly constrained optimization. It is capable of rapid asymptotic behavior and has relatively low storage requirements. Its application to large-scale nonlinear network optimization is discussed and computational results on problems of over 2000 variables and 1000 constraints are presented. Indications are that it could prove to be significantly better than known methods for this class of problems.

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