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Hi! Thank you for sharing the nice work. In the paper, you mentioned that the discrete {0, 1} constraint is relaxed to the continuous range [0, 1] for the Frank-Wolfe purpose. Then how do you map the continuous result back to satisfy the binary constraint? E.g., by doing row-maxing or something?
The text was updated successfully, but these errors were encountered:
Hi! Thank you for sharing the nice work. In the paper, you mentioned that the discrete {0, 1} constraint is relaxed to the continuous range [0, 1] for the Frank-Wolfe purpose. Then how do you map the continuous result back to satisfy the binary constraint? E.g., by doing row-maxing or something?
I have read the Matlab code. They applied the Hungarian algorithm to \hat{P} (the relaxed continuous permutation matrix) to get the binary version. You can find it in ZAC.m
Hi! Thank you for sharing the nice work. In the paper, you mentioned that the discrete {0, 1} constraint is relaxed to the continuous range [0, 1] for the Frank-Wolfe purpose. Then how do you map the continuous result back to satisfy the binary constraint? E.g., by doing row-maxing or something?
The text was updated successfully, but these errors were encountered: