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We then reformulate the energy functional as globally convex formulation to guarantee the global minima (not the local minima), which makes our model less sensitive to initialization.
How to show all local minima are approximate global minima.
Strong enough fields (B > 1.706) eliminate the barrier between local and global minima.
A heuristic "annealing schedule" is presented that is effective in finding global minima of error surfaces.
Trajectories pass in vicinity of the saddle point S and reach one of the two global minima M1 or M2.
Position of some points is indicated in the graph: Mloc - local minimum in the vicinity of the pole, M1, M2 - global minima, S - saddle point (position of the lowest energy barrier) between local and global minima, S* -other saddle point (higher in energy than S), Smin minimum energy barrier between global minima M1 and M2.
Various direct and inverse scattering problems require finding global minima of functions of several variables.
Some of the clusters are proposed initially and proved to be the real global minima.
But this learner may require a large number of trials to map the space and would not prioritize refinement of the global minima.
To be more specific, the condition for no escape is defined by the energy of lowest laying saddle between global minima M1 and M2.
Afterwards, to guarantee the global minima acquisition, two phases of optimization are designed, namely the preliminary and secondary optimization.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com