On the formulation and theory of the Newton interior-point method for nonlinear programming
Journal of Optimization Theory and Applications
Numerical experiments in semi-infinite programming
Computational Optimization 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
Semismooth Newton Methods for Solving Semi-Infinite Programming Problems
Journal of Global Optimization
A globally convergent primal-dual interior-point filter method for nonlinear programming
Mathematical Programming: Series A and B
A Smoothing Newton Method for Semi-Infinite Programming
Journal of Global Optimization
Line Search Filter Methods for Nonlinear Programming: Local Convergence
SIAM Journal on Optimization
Line Search Filter Methods for Nonlinear Programming: Motivation and Global Convergence
SIAM Journal on Optimization
Mathematical Programming: Series A and B
A homotopy interior point method for semi-infinite programming problems
Journal of Global Optimization
Generalized semi-infinite programming: A tutorial
Journal of Computational and Applied Mathematics
A smoothing projected Newton-type algorithm for semi-infinite programming
Computational Optimization and Applications
A new smoothing Newton-type algorithm for semi-infinite programming
Journal of Global Optimization
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Semi-infinite programming (SIP) problems can be efficiently solved by reduction type methods. Here, we present a new reduction method for SIP, where the multi-local optimization is carried out with a multi-local branch-and-bound method, the reduced (finite) problem is approximately solved by an interior point method, and the global convergence is promoted through a two-dimensional filter line search. Numerical experiments with a set of well-known problems are shown.