Line search methods
Author names: Elizabeth Conger
Steward: Dajun Yue and Fengqi You
Contents |
Introduction
An algorithm is a line search method if it seeks the minimum of a defined nonlinear function by selecting a reasonable direction vector that, when computed iteratively with a reasonable step size, will provide a function value closer to the absolute minimum of the function. Varying these will change the "tightness" of the optimization. For example, given the function , an initial
is chosen. To find a lower value of
, the value of
is increased by the following iteration scheme
in which is a positive scalar known as the step length and
defines the step direction.
Step Length
Choosing an appropriate step length has a large impact on the robustness of a line search method. To select the ideal step length, the following function could be minimized:
,
but this is not used in practical settings generally. This may give the most accurate minimum, but it would be very computationally expensive if the function has multiple local minima or stationary points, as shown in Figure 1.
Step Direction
Steepest Descent Method
Newton Method
Quasi-Newton Method
Conclusion
References
1. Sun, W. & Yuan, Y-X. (2006) Optimization Theory and Methods: Nonlinear Programming (Springer US) p 688.
2. Anonymous (2014) Line Search. (Wikipedia). http://en.wikipedia.org/wiki/Line_search.
3. Nocedal, J. & Wright, S. (2006) Numerical Optimization (Springer-Verlag New York, New York) 2 Ed p 664.