We study the problem of minimizing the number of critical simplices from the point of view of inapproximability and parameterized complexity. We first show inapproximability of Min-Morse Matching within a factor of $2^{\log^{(1-\epsilon)}n}$. Our second result shows that Min-Morse Matching is ${\bf W{[P]}}$-hard with respect to the standard parameter. Next, we show that Min-Morse Matching with standard parameterization has no FPT approximation algorithm for any approximation factor $\rho$. The above hardness results are applicable to complexes of dimension $\ge 2$. On the positive side, we provide a factor $O(\frac{n}{\log n})$ approximation algorithm for Min-Morse Matching on $2$-complexes, noting that no such algorithm is known for higher dimensional complexes. Finally, we devise discrete gradients with very few critical simplices for typical instances drawn from a fairly wide range of parameter values of the Costa-Farber model of random complexes.
翻译:我们从不协调性和参数化复杂度的角度来研究尽量减少关键 Simplices 数量的问题。 我们首先在 $2 的系数中显示 Min- Morse 匹配的不可接受性。 我们的第二个结果显示 Min- Morse 匹配在标准参数上是$\bf W{[P] ⁇ $- hard。 其次, 我们显示, Min- Morse 匹配标准参数化的 Min- Morice 没有为任何近似系数$\rho$的 FPT 近似算法。 以上硬性结果适用于维度的复杂值 $\ Ge 2 。 在正反面, 我们提供了 Min- Morse 匹配在 $2 Common- contracles 上的一个系数 $O(\ frac{ nlog n} $美元近似算算法, 指出对于更高维度的复合体没有这种算法。 最后, 我们设计离性梯度梯度的精确性梯度, 对于从一个相当广泛的参数值中提取的典型例子来说, 。