TY - JOUR
T1 - A feasible descent algorithm for solving variational inequality problems
AU - Han, Deren
AU - Lo, Hong K.
PY - 2003/11
Y1 - 2003/11
N2 - In this paper, for solving variational inequality problems (VIPs) we propose a feasible descent algorithm via minimizing the regularized gap function of Fukushima. Under the condition that the underlying mapping of VIP is strongly monotone, the algorithm is globally convergent for any regularized parameter, which is nice because it bypasses the necessity of knowing the modulus of strong monotonicity, a knowledge that is requested by other similar algorithms. Some preliminary computational results show the efficiency of the proposed method.
AB - In this paper, for solving variational inequality problems (VIPs) we propose a feasible descent algorithm via minimizing the regularized gap function of Fukushima. Under the condition that the underlying mapping of VIP is strongly monotone, the algorithm is globally convergent for any regularized parameter, which is nice because it bypasses the necessity of knowing the modulus of strong monotonicity, a knowledge that is requested by other similar algorithms. Some preliminary computational results show the efficiency of the proposed method.
KW - Feasible descent methods
KW - Global convergence
KW - Regularized gap functions
KW - Strongly monotone variational inequalities
UR - https://www.scopus.com/pages/publications/21144440309
U2 - 10.1007/s001860300282
DO - 10.1007/s001860300282
M3 - 文章
AN - SCOPUS:21144440309
SN - 1432-2994
VL - 58
SP - 259
EP - 269
JO - Mathematical Methods of Operations Research
JF - Mathematical Methods of Operations Research
IS - 2
ER -