Understand
There has been much recent interest in finding unconstrained local minima of smooth functions, due in part of the prevalence of such problems in machine learning and robust statistics.
- A particular focus is algorithms with good complexity guarantees.
- Second-order Newton-type methods that make use of regularization and trust regions have been analyzed from such a perspective.
- More recent proposals, based chiefly on first-order methodology, have also been shown to enjoy optimal iteration complexity rates, while providing additional guarantees on computational cost.
Built on
T. Steihaug
1983
Earlier work this paper cites.
J. Kuczyński and H. Woźniakowski
1992
Earlier work this paper cites.
A. R. Conn, N. I. M. Gould, and P. L. Toint
2000
Earlier work this paper cites.
Y. Nesterov and B. T. Polyak
2006
Earlier work this paper cites.
J. Nocedal and S. J. Wright
2006
Earlier work this paper cites.
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