A Simple Primal-Dual Feasible Interior-Point Method for Nonlinear Programming with Monotone Descent
Computational Optimization and Applications
Newton-KKT interior-point methods for indefinite quadratic programming
Computational Optimization and Applications
Local analysis of the feasible primal-dual interior-point method
Computational Optimization and Applications
An aggregate deformation homotopy method for min-max-min problems with max-min constraints
Computational Optimization and Applications
Primal-dual interior-point method for thermodynamic gas-particle partitioning
Computational Optimization and Applications
A feasible directions method for nonsmooth convex optimization
Structural and Multidisciplinary Optimization
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An exact-penalty-function-based scheme---inspired from an old idea due to Mayne and Polak [Math. Program., 11 (1976), pp. 67--80]---is proposed for extending to general smooth constrained optimization problems any given feasible interior-point method for inequality constrained problems. It is shown that the primal-dual interior-point framework allows for a simpler penalty parameter update rule than the one discussed and analyzed by the originators of the scheme in the context of first order methods of feasible direction. Strong global and local convergence results are proved under mild assumptions. In particular, (i) the proposed algorithm does not suffer a common pitfall recently pointed out by Wächter and Biegler [Math. Program., 88 (2000), pp. 565--574]; and (ii) the positive definiteness assumption on the Hessian estimate, made in the original version of the algorithm, is relaxed, allowing for the use of exact Hessian information, resulting in local quadratic convergence. Promising numerical results are reported.