随着动态规划在 OI 中的广泛运用，动态规划问题已经不再停滞于能够写出

方程就能得到完美解答。如今考察我们的对于动态规划的运用往往是考察动态规

划的优化，也就是降维。我们已经知道维护方程中的决策可以选择用数据结构进

行优化，比如：Splay、线段树，等等。这样的优化仅能将方程的时间复杂度下

降一个 LogN 的级别。如果 N 的范围相当大，即使下降一个 LogN 的级别也依然

超时呢？我们引进一种更强的优化——斜率优化。-With the extensive use of dynamic programming in the OI, dynamic programming problem is no longer able to write at a standstill

Equations can get the perfect answer. Today, we examine the use of dynamic programming for the study of dynamic regulation is often

Optimization scheme, that is, dimension reduction. We already know that the decision to maintain the equation into the data structure can be selected

Be optimized, for example: Splay, tree line, and the like. Only the next time complexity of this optimization equation

Drop a LogN level. If the range of N is quite large, even if the decline in the level also remains a LogN

Overtime it? We introduce a stronger optimization- slope optimization.