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Furthermore, it can be shown that whenever the method enters the region of local convergence, the method converges to a full step procedure and thus λ → 1 [ 17, 22, 23].
We assume that the method converges to the exact solution of the problem if there is convergence at all.
The method converges to Pareto-optimal points.
We show that our method converges to the optimal price faster than standard machine learning MAB solutions to the problem.
Theoretical results ensure that the proposed method converges to a global minimizer.
In the case of exhaustive sampling, the steering kernel regression method converges to the true solution.
The proposed method converges to the original value in one cycle in presence of harmonics.
Theoretical analysis shows that the new method converges to the unique solution of the linear system.
We solve it using a splitting approach that is discussed in detail in the Supplementary Methods, along with comparisons to state-of-the-art methods demonstrating that our method converges to a significantly better optimum in drastically less computational time.
The optimization method converges to a unique solution for specific values of evaporation and condensation temperatures and geothermal and cooling water velocities.
The results show that the method converges to a minimum PPF and a prescribed k in an efficient and accurate form.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com