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Exact(16)
where Γ and Ω are positive definite cost matrices [19].
The cost matrices of the test problems are given in Tables 5 and 6.
The positive definite cost matrices Γ and Ω are both equal to the identity matrix.
In these experiments, we tried two different cost matrices Figure 13 Mean-squared errors as a function of κ.
In the considered example, there are five possible cost matrices that have to be optimised by the Hungarian algorithm.
The blue (square) and red (diamond) correspond for the cost matrices Q and Qpos, respectively as defined in Equation 35.
Similar(44)
Then, a cost matrix is calculated from the distance matrix.
Learned model is optimized on cost matrix; 4.
Generate accumulative cost matrix according to (13) or (14) 3.
That way the cost matrix is stretched to size N × N.
The method takes a cost matrix and a base evaluator.
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