A Modified Augmented Lagrange Multiplier Method for Large-Scale Optimization

A modified augmented Lagrange multiplier method for large-scale nonlinear optimization problem is studied in this paper. The basic steps of the proposed algorithm comprise an outer iteration, in which the Lagrange multipliers and various penalty parameters are updated, and an inner iteration, in which a nonlinear optimization with simple bound constraints is solved. The algoritm presented has been implemented in a general routine denoted as MALMM. Numerical results are presented for a variety of problems, which show the stability and effectiveness of the algorithm in large-scale problems.

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