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東京大学 新領域創成科学研究科 メディカル情報生命専攻 2023年8月実施 問題7

Author​

zephyr, 祭音Myyura

Description​

For real number nn (>1)(> 1) and non-negative integer ii, let f(i)(n)f^{(i)}(n) denote the function ff applied ii times iteratively to nn; namely, f(0)(n)=nf^{(0)}(n) = n and f(i)(n)=f(f(i−1)(n))f^{(i)}(n) = f(f^{(i-1)}(n)) if i>0i > 0. If f(i)(n)≤1f^{(i)}(n) \leq 1 for some ii, define f∗(n)f^{*}(n) as the minimum such ii; namely, f∗(n)=min⁡{i>0∣f(i)(n)≤1}f^{*}(n) = \min \{ i > 0 \mid f^{(i)}(n) \leq 1 \}. Otherwise, f∗(n)f^{*}(n) is undefined. Answer each of the following questions and the reasoning behind it.

(1) When f(n)=n2f(n) = \frac{n}{2}, show f∗(n)=⌈log⁡2n⌉f^{*}(n) = \lceil \log_2 n \rceil (⌈x⌉\lceil x \rceil is the minimum integer that is xx or more).

(2) When f(n)=log⁡2nf(n) = \log_2 n, answer the minimum value of nn that satisfies f∗(n)=5f^{*}(n) = 5.

(3) When f(n)=log⁡2nf(n) = \log_2 n, which is greater or equal, f(f∗(n))f(f^{*}(n)) or f∗(f(n))f^{*}(f(n))?


对于实数 nn (>1)(> 1) 和非负整数 ii,设 f(i)(n)f^{(i)}(n) 表示将函数 ff 迭代地应用于 nn ii 次;即 f(0)(n)=nf^{(0)}(n) = n 并且 f(i)(n)=f(f(i−1)(n))f^{(i)}(n) = f(f^{(i-1)}(n)) 当 i>0i > 0。如果 f(i)(n)≤1f^{(i)}(n) \leq 1 对某些 ii 成立,定义 f∗(n)f^{*}(n) 为最小的 ii;即 f∗(n)=min⁡{i>0∣f(i)(n)≤1}f^{*}(n) = \min \{ i > 0 \mid f^{(i)}(n) \leq 1 \}。否则,f∗(n)f^{*}(n) 未定义。回答以下每个问题并解释其背后的理由。

(1) 当 f(n)=n2f(n) = \frac{n}{2} 时,证明 f∗(n)=⌈log⁡2n⌉f^{*}(n) = \lceil \log_2 n \rceil (⌈x⌉\lceil x \rceil 是大于或等于 xx 的最小整数)。

(2) 当 f(n)=log⁡2nf(n) = \log_2 n 时,回答满足 f∗(n)=5f^{*}(n) = 5 的 nn 的最小值。

(3) 当 f(n)=log⁡2nf(n) = \log_2 n 时,比较 f(f∗(n))f(f^{*}(n)) 和 f∗(f(n))f^{*}(f(n)) 哪个更大或相等?

题目描述​

对实数 n>1n>1 和非负整数 ii,定义函数迭代

f(0)(n)=n,f(i)(n)=f(f(i−1)(n))(i>0).f^{(0)}(n)=n,\qquad f^{(i)}(n)=f(f^{(i-1)}(n))\quad(i>0).

若存在 ii 使 f(i)(n)≤1f^{(i)}(n)\le1,定义

f∗(n)=min⁡{i>0∣f(i)(n)≤1};f^*(n)=\min\{i>0\mid f^{(i)}(n)\le1\};

若不存在则 f∗(n)f^*(n) 未定义。对每问给出答案及理由:

  1. 当 f(n)=n/2f(n)=n/2 时,证明

    f∗(n)=⌈log⁡2n⌉,f^*(n)=\lceil\log_2n\rceil,

    其中 ⌈x⌉\lceil x\rceil 为不小于 xx 的最小整数。

  2. 当 f(n)=log⁡2nf(n)=\log_2n 时,求满足 f∗(n)=5f^*(n)=5 的最小 nn。

  3. 仍取 f(n)=log⁡2nf(n)=\log_2n,比较 f(f∗(n))f(f^*(n)) 与 f∗(f(n))f^*(f(n)) 的大小关系。

Kai​

Written by zephyr

解题思路​

这个问题涉及迭代函数的分析,主要考察了对数函数的性质和迭代过程的理解。我们需要仔细分析每个子问题,利用函数迭代和对数的性质来解决。问题 (1) 和 (2) 为问题 (3) 的解答铺垫,因为它们帮助我们理解 f∗f^* 函数的行为。

对于问题 (3),我们需要更仔细地分析 f(f∗(n))f(f^*(n)) 和 f∗(f(n))f^*(f(n)) 的关系。这需要我们对不同范围的 n 进行分类讨论,因为 f(n)=log⁡2nf(n) = \log_2 n 的行为在不同的 n 值范围内会有所不同。

1. Proving f∗(n)=⌈log⁡2n⌉f^*(n) = \lceil \log_2 n \rceil when f(n)=n2f(n) = \frac{n}{2}​

To prove this, we need to show that ⌈log⁡2n⌉\lceil \log_2 n \rceil is the minimum number of iterations needed to reduce nn to a value less than or equal to 1.

Let k=⌈log⁡2n⌉k = \lceil \log_2 n \rceil. By definition, 2k−1<n≤2k2^{k-1} < n \leq 2^k.

Applying ff iteratively kk times:

f(k)(n)=n2k≤2k2k=1f^{(k)}(n) = \frac{n}{2^k} \leq \frac{2^k}{2^k} = 1

This shows that kk iterations are sufficient to reduce nn to a value ≤1\leq 1.

Now, let's consider k−1k-1 iterations:

f(k−1)(n)=n2k−1>2k−12k−1=1f^{(k-1)}(n) = \frac{n}{2^{k-1}} > \frac{2^{k-1}}{2^{k-1}} = 1

This shows that k−1k-1 iterations are not enough to reduce nn to a value ≤1\leq 1.

Therefore, k=⌈log⁡2n⌉k = \lceil \log_2 n \rceil is the minimum number of iterations needed, proving that f∗(n)=⌈log⁡2n⌉f^*(n) = \lceil \log_2 n \rceil.

2. Finding minimum nn such that f∗(n)=5f^*(n) = 5 when f(n)=log⁡2nf(n) = \log_2 n​

Exactly five iterations means f(4)(n)>1f^{(4)}(n)>1 and f(5)(n)≤1f^{(5)}(n)\le1. Applying the increasing inverse function 2x2^x repeatedly gives

216<n≤265536.2^{16}<n\le 2^{65536}.

Since nn is real, there is no minimum; the infimum is 216=655362^{16}=65536. If nn were restricted to integers, the minimum would be 6553765537.

3. Comparing f(f∗(n))f(f^*(n)) and f∗(f(n))f^*(f(n)) when f(n)=log⁡2nf(n) = \log_2 n​

Let's analyze these two expressions by considering different ranges of n:

  1. f(f∗(n))f(f^*(n)): This applies log⁡2\log_2 to the number of iterations needed to reduce nn to ≤1\leq 1.
  2. f∗(f(n))f^*(f(n)): This finds the number of iterations needed to reduce log⁡2n\log_2 n to ≤1\leq 1.

Let's consider different cases:

Case 1: 1<n≤21<n\le2

  • f∗(n)=1f^*(n)=1 and f(f∗(n))=0f(f^*(n))=0.
  • However, f(n)≤1f(n)\le1, so f∗(f(n))f^*(f(n)) is outside the stated domain of f∗f^* and is undefined.

Case 2: 2<n≤42 < n \leq 4

  • In this case, f∗(n)=2f^*(n) = 2, because two applications of log⁡2\log_2 are needed to reduce nn to ≤1\leq 1.
  • f(f∗(n))=f(2)=1f(f^*(n)) = f(2) = 1
  • f∗(f(n))=f∗(log⁡2n)=1f^*(f(n)) = f^*(\log_2 n) = 1, because 1<log⁡2n≤21 < \log_2 n \leq 2
  • Therefore, when 2<n≤42 < n \leq 4, f(f∗(n))=f∗(f(n))=1f(f^*(n)) = f^*(f(n)) = 1

Case 3: n>4n > 4

  • In this case, f∗(n)≥3f^*(n) \geq 3
  • f(f∗(n))=log⁡2(f∗(n))f(f^*(n)) = \log_2(f^*(n))
  • f∗(f(n))=f∗(log⁡2n)=f∗(n)−1f^*(f(n)) = f^*(\log_2 n) = f^*(n) - 1

For n>4n > 4, we can prove that f(f∗(n))<f∗(f(n))f(f^*(n)) < f^*(f(n)):

Let k=f∗(n)k = f^*(n).

f(f∗(n))=log⁡2k<k−1=f∗(n)−1=f∗(f(n))f(f^*(n)) = \log_2 k < k - 1 = f^*(n) - 1 = f^*(f(n))

This inequality holds because for any integer k≥3k \geq 3, log⁡2k<k−1\log_2 k < k - 1.

Therefore, we can conclude:

  • When 1<n≤21 < n \leq 2: f(f∗(n))=0f(f^*(n))=0, while f∗(f(n))f^*(f(n)) is undefined.
  • When 2<n≤42 < n \leq 4: f(f∗(n))=f∗(f(n))f(f^*(n)) = f^*(f(n))
  • When n>4n > 4: f(f∗(n))<f∗(f(n))f(f^*(n)) < f^*(f(n))

In summary, where both sides are defined, equality holds for 2<n≤42<n\le4, and f(f∗(n))<f∗(f(n))f(f^*(n))<f^*(f(n)) for n>4n>4.

Knowledge​

难点思路​

问题 (2) 中,从 f∗(n)=5f^*(n) = 5 反推 nn 的最小值需要仔细思考迭代过程。我们需要从最后一步开始,逐步应用指数函数来得到最终结果。

解题技巧和信息​

  1. 在处理迭代函数时,正向和反向思考都很重要。有时从最终状态反推初始值会更容易。
  2. 对数函数和指数函数的性质在这类问题中经常用到,熟悉它们的特性和关系很重要。
  3. 在比较复杂函数的大小关系时,考虑极限情况和通用情况都很重要。
  4. 在分析复杂函数关系时,分类讨论是一个强有力的工具。它可以帮助我们处理不同范围内的特殊情况。
  5. 对于涉及对数和整数的问题,特别注意整数边界情况(如 2 的整数次幂)往往很重要。
  6. 在证明不等式时,有时需要考虑具体的数值范围,而不仅仅是一般情况。

重点词汇​

  • iterative function 迭代函数
  • ceiling function 天花板函数
  • logarithm 对数
  • exponentiation 指数运算
  • inequality 不等式

参考资料​

  1. Concrete Mathematics: A Foundation for Computer Science (2nd Edition) by Ronald L. Graham, Donald E. Knuth, and Oren Patashnik, Chapter 3: Integer Functions
  2. Introduction to Algorithms (3rd Edition) by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, Chapter 3: Growth of Functions