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A primal truncated Newton algorithm with application to large-scale nonlinear network optimization. (English) Zbl 0635.90072

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 applications 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.

MSC:

90C30 Nonlinear programming
90C35 Programming involving graphs or networks
65K05 Numerical mathematical programming methods
90C06 Large-scale problems in mathematical programming
49M37 Numerical methods based on nonlinear programming

Software:

CONOPT
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