Abstract
This paper describes a method for constrained optimization which obtains its search directions from a quadratic programming subproblem based on the well-known augmented Lagrangian function. The method can be viewed as a development of the algorithm REQP which is related to the classical exterior point penalty function: and it is argued that the new technique will have certain computational advantages arising from the fact that it need not involve a sequence of penalty parameters tending to zero. An algorithm with global convergence for equality constrained problems is presented. Computational results are also given for this algorithm and some alternative strategies are briefly considered for extending it to deal with inequality constraints.
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Bartholomew-Biggs, M.C. (1987). Recursive quadratic programming methods based on the augmented lagrangian. In: Hoffman, K.L., Jackson, R.H.F., Telgen, J. (eds) Computation Mathematical Programming. Mathematical Programming Studies, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0121177
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DOI: https://doi.org/10.1007/BFb0121177
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