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A Globally Convergent Interior-Point Algorithm for Nonlinear Programming Problems

By I. Akrotirianakis and B. Rustem


Abstract. This paper presents a primal-dual interior-point algorithm for solving general constrained nonlinear programming problems. The inequality constraints are incorporated into the objective function by means of a logarithmic barrier function. Also, satisfaction of the equality constraints is enforced through the use of an adaptive quadratic penalty function. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Finally, extensive computational results show that the algorithm can solve large and difficult problems in an efficient and robust way. Key Words. Primal-dual interior-point algorithms, merit functions, convergence theory. 1

Year: 1997
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