Unconstrained nonlinear minimization and the Richards function

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dc.contributor.authorPhua, Kang-Hohen_US
dc.date.accessioned2024-08-15T17:16:46Z
dc.date.available2024-08-15T17:16:46Z
dc.date.copyright1971en_US
dc.date.issued1971
dc.degree.departmentDepartment of Mathematics and Statistics
dc.degree.levelMaster of Science M.Sc.en
dc.description.abstractA 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.en
dc.format.extent92 pages
dc.identifier.urihttps://hdl.handle.net/1828/19348
dc.rightsAvailable to the World Wide Weben_US
dc.titleUnconstrained nonlinear minimization and the Richards functionen_US
dc.typeThesisen_US

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