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Fluorescence quantum yields were estimated by adopting a gradient method.
An objective function is minimized using a gradient method.
A gradient method will usually terminate in this situation and so fail to find the solution.
A gradient method gives the maximum of this surface, which belongs to the Ernst ellipsoid.
The inverse problem relies on a classic local optimization approach implemented with a gradient method.
Delosme [56] proposed a gradient method for search in a localization procedure leading to computation of optimal source locations from noisy TDOA's.
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A linear gradient method was applied using a solution of distilled water with 0.1% TFA (solvent A) and methanol (solvent B).
This paper is concerned with a conjugate gradient method that utilizes a successive set of preconditioner matrices.
First, the systems were energy-minimized using a conjugate gradient method coupled to a line-search algorithm for 50000 steps, which was more than sufficient to reach convergence.
We study such a data-driven method based on a stochastic gradient method.
A linear gradient method was used with solvent A (15% isopropanol, 100 mM ammonium acetate pH 4.7) and solvent B (60% isopropanol, 100 mM ammonium acetate, pH 4.7).
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