TY - JOUR
T1 - Improved Hessian approximations for the limited memory BFGS method
AU - Al-Baali, Mehiddin
PY - 1999
Y1 - 1999
N2 - This paper considers simple modifications of the limited memory BFGS (L-BFGS) method for large scale optimization. It describes algorithms in which alternating ways of re-using a given set of stored difference vectors are outlined. The proposed algorithms resemble the L-BFGS method, except that the initial Hessian approximation is defined implicitly like the L-BFGS Hessian in terms of some stored vectors rather than the usual choice of a multiple of the unit matrix. Numerical experiments show that the new algorithms yield desirable improvement over the L-BFGS method.
AB - This paper considers simple modifications of the limited memory BFGS (L-BFGS) method for large scale optimization. It describes algorithms in which alternating ways of re-using a given set of stored difference vectors are outlined. The proposed algorithms resemble the L-BFGS method, except that the initial Hessian approximation is defined implicitly like the L-BFGS Hessian in terms of some stored vectors rather than the usual choice of a multiple of the unit matrix. Numerical experiments show that the new algorithms yield desirable improvement over the L-BFGS method.
KW - BFGS updating formula
KW - Large scale optimization
KW - Limited memory BFGS method
KW - Quasi-Newton methods
UR - http://www.scopus.com/inward/record.url?scp=0033408125&partnerID=8YFLogxK
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U2 - 10.1023/A:1019142304382
DO - 10.1023/A:1019142304382
M3 - Article
AN - SCOPUS:0033408125
SN - 1017-1398
VL - 22
SP - 99
EP - 112
JO - Numerical Algorithms
JF - Numerical Algorithms
IS - 1
ER -