A new limited memory method for unconstrained nonlinear least squares
Keyword(s):
AbstractThis paper suggests a new limited memory trust region algorithm for large unconstrained black box least squares problems, called LMLS. Main features of LMLS are a new non-monotone technique, a new adaptive radius strategy, a new Broyden-like algorithm based on the previous good points, and a heuristic estimation for the Jacobian matrix in a subspace with random basis indices. Our numerical results show that LMLS is robust and efficient, especially in comparison with solvers using traditional limited memory and standard quasi-Newton approximations.
1999 ◽
Vol 105
(2-3)
◽
pp. 183-194
◽
Keyword(s):
Keyword(s):
1991 ◽
Vol 34
(3)
◽
pp. 287-305
◽
Keyword(s):
2001 ◽
Vol 129
(1-2)
◽
pp. 1-14
◽
Keyword(s):
2009 ◽
Vol 2009
◽
pp. 1-17
Keyword(s):
2018 ◽
Vol 178
(3)
◽
pp. 824-859
◽
Keyword(s):
Keyword(s):
2018 ◽
Vol 14
(2)
◽
pp. 707-718
Keyword(s):