Unconstrained nonlinear minimization and the Richards function
Date
1971
Authors
Phua, Kang-Hoh
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Abstract
A survey of unconstrained nonlinear optimization techniques is made. Some of these methods are then applied to solve the problem of fitting the Richards function to experimental data by the method of least-squares.
In attempting to solve this problem, the dimensions of the problem are first reduced from 4 to 2, by solving the linear parameters in terms of the nonlinear parameter s from the so-called normal equations. Numerical results show that this approach is more efficient. Moreover, a comparison is made between the performances of the selected algorithms in solving this problem.