Predictor-corrector interior-point algorithms for linear constrained optimization problem are described. The algorithms are based on Mehrotra's predictor-corrector method, in the context of linear programming, and are modified to take into account the nonlinearity of the objective function. The convergence of the algorithms, as well as some other properties are proved. Computational results are provided for a number of linear constrained optimization problems in comparison with some codes such as TOLMIN (Powell), MINOS (Murtagh & Saunders). SPENBAR (Andrei), NLPQL (Schittkowski).
linear programming, interior- point, predictor-corrector, linear optimization.
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