SIAM Journal on Control and Optimization
On the formulation and theory of the Newton interior-point method for nonlinear programming
Journal of Optimization Theory and Applications
An Interior-Point Algorithm for Nonconvex Nonlinear Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
An Interior Point Algorithm for Large-Scale Nonlinear Programming
SIAM Journal on Optimization
Primal-Dual Interior Methods for Nonconvex Nonlinear Programming
SIAM Journal on Optimization
SIAM Journal on Optimization
Newton-KKT interior-point methods for indefinite quadratic programming
Computational Optimization and Applications
Improving ultimate convergence of an augmented Lagrangian method
Optimization Methods & Software - Dedicated to Professor Michael J.D. Powell on the occasion of his 70th birthday
Computers & Mathematics with Applications
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We propose and analyze a primal-dual interior point method of the “feasible” type, with the additional property that the objective function decreases at each iteration. A distinctive feature of the method is the use of different barrier parameter values for each constraint, with the purpose of better steering the constructed sequence away from non-KKT stationary points. Assets of the proposed scheme include relative simplicity of the algorithm and of the convergence analysis, strong global and local convergence properties, and good performance in preliminary tests. In addition, the initial point is allowed to lie on the boundary of the feasible set.