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After the relaxation transformation, the problem of (11) is still a non-convex optimization problem owing to the presence of auxiliary variable τ.
Thanks to the mentioned transformation, the problem (5)–(6) can be reformulated as: FT^{RT} = left[ {left( {lambda_{t}^{RT} (omega )Delta P_{t}^ (omega )} right) - left( {lambda_{t}^{RT} (omega )Delta P_{t}^ (omega )} right)} right] (8) left{ {begin{array}{*{20}l} {{text{s}}.
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By using variable transformations, the problem is converted into linear Volterra integral equations of the second kind.
In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided.
With the help of a function transformation, the nonlinear problem given is transformed into a linear problem and then the back-ward Euler scheme is constructed for the latter.
The technique permits the transformation of the problem, initially non-deterministic, into two independents deterministic sub-problems.
We make a dual transformation for the problem to obtain a new free boundary problem with a linear equation.
The transformation of the problem into the multi-resolution wavelet domain provides a set of coupled linear equations.
This allows for the transformation of the problem of the design of a reactionless manipulator into a problem of balancing pivoted legs carrying concentrated masses.
For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem.
Therefore, we will plan to make improvements to our approach to adapt it to this new transformation of the problem, and study its effects on the makespan.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com