polynomial time

多项式时间是指一个算法在输入规模为n的情况下，可以在多项式级别的时间复杂度内解决问题。换句话说，多项式时间算法的运行时间可以表示为一个多项式函数。

在计算机科学中，多项式时间算法被认为是高效的，因为它们的运行时间随着输入规模的增加而以多项式的方式增长，而不是指数或阶乘级别的增长。

引用中给出了多项式时间算法的定义，即存在一个多项式p(|x|)，对于任何输入x∈{0,1}*，算法在计算A(x)时至多花费p(|x|)步。

引用介绍了多项式时间内判断素数的算法。

引用中提到了概率性多项式时间算法，这种算法在多项式时间内运行，但同时也可以访问某些提供真随机位的预言机。与确定性算法不同，概率性算法的输出是一个随机变量，可能具有不同值的一定概率。

相关问题

two trees, t1 and t2, are isomorphic if t1 can be transformed into t2 by swapping left and right children of (some of the) nodes in t1. for instance, the two trees in figure 1 are isomorphic because they are the same if the children of a, b, and g, but not the other nodes, are swapped. give a polynomial time algorithm to decide if two trees are isomorphic.

两棵树t1和t2是同构的，如果t1可以通过交换其某些节点的左右子节点来转换为t2。例如，图1中的两棵树是同构的，因为如果交换a、b和g的子节点，但不交换其他节点，它们就是相同的。给出一个多项式时间算法来判断两棵树是否同构。

polynomial space-time covariance matrix

A polynomial space-time covariance matrix is a matrix that describes the covariance between different space-time points in a polynomial regression model. In other words, it is a matrix that describes how closely related different space-time points are in a polynomial regression context.

The polynomial space-time covariance matrix can be used for various purposes, including spatial interpolation, prediction, and uncertainty analysis. It is commonly used in geostatistics, where it is used to model the spatial variability of different variables across a region.

The polynomial space-time covariance matrix is typically defined as a function of the distance and time between different space-time points, and it can be estimated using various methods, including maximum likelihood and Bayesian methods. Once estimated, the covariance matrix can be used to generate predictions for future space-time points, as well as to quantify the uncertainty associated with those predictions.