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其中<math>\alpha\in(0,2)</math>是参数。实际上，当<math>\alpha\ge1</math>时，<math>\Gamma_{\alpha}</math>是P的[[沙滕范数]]（Schatten norm）；当<math>0<\alpha<1</math>时，<math>\Gamma_{\alpha}</math>是P的准范数（quasinorm）<ref>Schatten norm from Wikipedia. https://en.wikipedia.org/wiki/Schatten norm</ref><ref>Recht, B., Fazel, M., Parrilo, P.A.: Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM review 52(3), 471–501 (2010)</ref><ref>Chi, Y., Lu, Y.M., Chen, Y.: Nonconvex optimization meets low-rank matrix factorization: An overview. IEEE Transactions on Signal Processing 67(20), 52395269 (2019)</ref><ref name="Cui">Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., Tian, Q.: Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3941–3950 (2020)</ref>。使用这个定义来刻画近似动力学可逆性是合理的，因为完全动力学可逆性可以通过最大化<math>\Gamma_{\alpha}</math>来得到。我们可以证明如下结论：对于任意<math>\alpha\in(0,2)</math>，<math>\Gamma_{\alpha}</math>的最大值是N，当且仅当P是置换矩阵的时候能取到该最大值。

其中<math>\alpha\in(0,2)</math>是参数。实际上，当<math>\alpha\ge1</math>时，<math>\Gamma_{\alpha}</math>是P的[[沙滕范数]]（Schatten norm）；当<math>0<\alpha<1</math>时，<math>\Gamma_{\alpha}</math>是P的准范数（quasinorm）<ref>Schatten norm from Wikipedia. https://en.wikipedia.org/wiki/Schatten norm</ref><ref>Recht, B., Fazel, M., Parrilo, P.A.: Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM review 52(3), 471–501 (2010)</ref><ref>Chi, Y., Lu, Y.M., Chen, Y.: Nonconvex optimization meets low-rank matrix factorization: An overview. IEEE Transactions on Signal Processing 67(20), 52395269 (2019)</ref><ref name="Cui">Cui, S., Wang, S., Zhuo, J., Li, L., Huang, Q., Tian, Q.: Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3941–3950 (2020)</ref>。使用这个定义来刻画近似动力学可逆性是合理的，因为完全动力学可逆性可以通过最大化<math>\Gamma_{\alpha}</math>来得到。我们可以证明如下结论：对于任意<math>\alpha\in(0,2)</math>，<math>\Gamma_{\alpha}</math>的最大值是N，当且仅当P是置换矩阵的时候能取到该最大值。