TY - JOUR
T1 - Interior-point methods for nonconvex nonlinear programming
T2 - Filter methods and merit functions
AU - Benson, Hande Y.
AU - Vanderbei, Robert J.
AU - Shanno, David F.
N1 - Funding Information:
Research of the first and third authors supported by NSF grant DMS-9870317, ONR grant N00014-98-1-0036. Research of the second author supported by NSF grant DMS-9805495.
PY - 2002/11
Y1 - 2002/11
N2 - 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.
AB - 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.
KW - Filter methods
KW - Interior-point methods
KW - Nonconvex optimization
KW - Nonlinear programming
UR - http://www.scopus.com/inward/record.url?scp=0036854804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036854804&partnerID=8YFLogxK
U2 - 10.1023/A:1020533003783
DO - 10.1023/A:1020533003783
M3 - Article
AN - SCOPUS:0036854804
SN - 0926-6003
VL - 23
SP - 257
EP - 272
JO - Computational Optimization and Applications
JF - Computational Optimization and Applications
IS - 2
ER -