Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a filter-based approach in an interior-point algorithm. Extensive numerical testing shows that such an approach is more efficient than using a merit function alone.
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Benson, H.Y., Vanderbei, R.J. & Shanno, D.F. Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions. Computational Optimization and Applications 23, 257–272 (2002). https://doi.org/10.1023/A:1020533003783
- interior-point methods
- nonconvex optimization
- nonlinear programming
- filter methods