Solving mathematical programs with complementarity constraints as nonlinear programs

TY - JOUR

T1 - Solving mathematical programs with complementarity constraints as nonlinear programs

AU - Fletcher, Roger

AU - Leyffer, Sven

PY - 2004/2/1

Y1 - 2004/2/1

N2 - We consider solving mathematical programs with complementarity constraints (MPCCs) as nonlinear programs (NLPs) using standard NLP solvers. This approach is appealing because it allows existing off-the-shelf NLP solvers to tackle large instances of MPCCs. Numerical experience on MacMPEC, a large collection of MPCC test problems is presented. Our experience indicates that sequential quadratic programming (SQP) methods are very well suited for solving MPCCs and at present outperform interior-point solvers both in terms of speed and reliability. All NLP solvers also compare very favorably to special MPCC solvers on tests published in the literature.

AB - We consider solving mathematical programs with complementarity constraints (MPCCs) as nonlinear programs (NLPs) using standard NLP solvers. This approach is appealing because it allows existing off-the-shelf NLP solvers to tackle large instances of MPCCs. Numerical experience on MacMPEC, a large collection of MPCC test problems is presented. Our experience indicates that sequential quadratic programming (SQP) methods are very well suited for solving MPCCs and at present outperform interior-point solvers both in terms of speed and reliability. All NLP solvers also compare very favorably to special MPCC solvers on tests published in the literature.

KW - Complementarity constraints

KW - Interior-point methods

KW - MPCC

KW - Nonlinear programming

KW - Sequential quadratic programming

UR - https://www.scopus.com/pages/publications/1542346047

U2 - 10.1080/10556780410001654241

DO - 10.1080/10556780410001654241

M3 - Article

AN - SCOPUS:1542346047

SN - 1055-6788

VL - 19

SP - 15

EP - 40

JO - Optimization Methods and Software

JF - Optimization Methods and Software

IS - 1 SPEC. ISS.

ER -