A Mixed Logarithmic Barrier-Augmented Lagrangian Method for Nonlinear Optimization

We present a primal–dual algorithm for solving a constrained optimization problem. This method is based on a Newtonian method applied to a sequence of perturbed KKT systems. These systems follow from a reformulation of the initial problem under the form of a sequence of penalized problems, by introd...

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Vydáno v:Journal of optimization theory and applications Ročník 173; číslo 2; s. 523 - 547
Hlavní autoři: Armand, Paul, Omheni, Riadh
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2017
Springer Nature B.V
Springer Verlag
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ISSN:0022-3239, 1573-2878
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Shrnutí:We present a primal–dual algorithm for solving a constrained optimization problem. This method is based on a Newtonian method applied to a sequence of perturbed KKT systems. These systems follow from a reformulation of the initial problem under the form of a sequence of penalized problems, by introducing an augmented Lagrangian for handling the equality constraints and a log-barrier penalty for the inequalities. We detail the updating rules for monitoring the different parameters (Lagrange multiplier estimate, quadratic penalty and log-barrier parameter), in order to get strong global convergence properties. We show that one advantage of this approach is that it introduces a natural regularization of the linear system to solve at each iteration, for the solution of a problem with a rank deficient Jacobian of constraints. The numerical experiments show the good practical performances of the proposed method especially for degenerate problems.
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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-017-1071-x