From cb2f8632ba152babb9bc7f9a8316aa6dd9ec4fff Mon Sep 17 00:00:00 2001 From: CyC2018 <1029579233@qq.com> Date: Thu, 19 Apr 2018 22:22:55 +0800 Subject: [PATCH] auto commit --- notes/Java 虚拟机.md | 2 +- notes/Leetcode 题解.md | 73 +++++++++++------- pics/61942711-45a0-4e11-bbc9-434e31436f33.png | Bin 0 -> 27648 bytes pics/a3da4342-078b-43e2-b748-7e71bec50dc4.png | Bin 0 -> 24576 bytes 4 files changed, 48 insertions(+), 27 deletions(-) create mode 100644 pics/61942711-45a0-4e11-bbc9-434e31436f33.png create mode 100644 pics/a3da4342-078b-43e2-b748-7e71bec50dc4.png diff --git a/notes/Java 虚拟机.md b/notes/Java 虚拟机.md index cf9a8bbc..852e171c 100644 --- a/notes/Java 虚拟机.md +++ b/notes/Java 虚拟机.md @@ -142,7 +142,7 @@ Java 对引用的概念进行了扩充,引入四种强度不同的引用类型 **(一)强引用** -只要强引用存在,垃圾回收器永远不会回收调掉被引用的对象。 +只要强引用存在,垃圾回收器永远不会回收被引用的对象。 使用 new 一个新对象的方式来创建强引用。 diff --git a/notes/Leetcode 题解.md b/notes/Leetcode 题解.md index 73f19098..3520557c 100644 --- a/notes/Leetcode 题解.md +++ b/notes/Leetcode 题解.md @@ -127,7 +127,7 @@ public int arrangeCoins(int n) { int l = 0, h = n; while(l <= h){ int m = l + (h - l) / 2; - long x = m * (m + 1L) / 2; + long x = m * (m + 1) / 2; if(x == n) return m; else if(x < n) l = m + 1; else h = m - 1; @@ -194,7 +194,7 @@ You need to output 2. 因为最小的孩子最容易得到满足,因此先满足最小孩子。给一个孩子的饼干应当尽量小又能满足该孩子,这样大饼干就能拿来给满足度比较大的孩子。 -证明:假设在某次选择中,贪心策略选择给第 i 个孩子分配第 m 个饼干,并且第 i 个孩子满足度最小,第 m 个饼干为可以满足第 i 个孩子的最小饼干,利用贪心策略最终可以满足 k 个孩子。假设最优策略在这次选择中给 i 个孩子分配第 n 个饼干,并且这个饼干大于第 m 个饼干。我们发现使用第 m 个饼干去替代第 n 个饼干完全不影响后续的结果,因此不存在比贪心策略更优的策略,即贪心策略就是最优策略。 +证明:假设在某次选择中,贪心策略选择给第 i 个孩子分配第 m 个饼干,并且第 i 个孩子满足度最小,第 m 个饼干为可以满足第 i 个孩子的最小饼干。假设最优策略在这次选择中给 i 个孩子分配第 n 个饼干,并且这个饼干大于第 m 个饼干。我们发现使用第 m 个饼干去替代第 n 个饼干完全不影响后续的结果,因此不存在比贪心策略更优的策略,即贪心策略就是最优策略。 ```java public int findContentChildren(int[] g, int[] s) { @@ -1813,10 +1813,9 @@ dp[N] 即为所求。 ```java public int climbStairs(int n) { - if(n == 1) return 1; - if(n == 2) return 2; - int pre1 = 2, pre2 = 1; - for(int i = 2; i < n; i++){ + if (n <= 2) return n; + int pre2 = 1, pre1 = 2; + for (int i = 2; i < n; i++) { int cur = pre1 + pre2; pre2 = pre1; pre1 = cur; @@ -1940,7 +1939,7 @@ dp[N] 即为所求。

-对于一个长度为 N 的序列,最长子序列并不一定会以 SN 为结尾,因此 dp[N] 不是序列的最长递增子序列的长度,需要遍历 dp 数组找出最大值才是所要的结果,即 max{ dp[i] | 1 <= i <= N} 即为所求。 +对于一个长度为 N 的序列,最长递增子序列并不一定会以 SN 为结尾,因此 dp[N] 不是序列的最长递增子序列的长度,需要遍历 dp 数组找出最大值才是所要的结果,即 max{ dp[i] | 1 <= i <= N} 即为所求。 **最长递增子序列** @@ -1950,22 +1949,24 @@ dp[N] 即为所求。 public int lengthOfLIS(int[] nums) { int n = nums.length; int[] dp = new int[n]; - for(int i = 0; i < n; i++){ + for (int i = 0; i < n; i++) { int max = 1; - for(int j = 0; j < i; j++){ - if(nums[i] > nums[j]) max = Math.max(max, dp[j] + 1); + for (int j = 0; j < i; j++) { + if (nums[i] > nums[j]) { + max = Math.max(max, dp[j] + 1); + } } dp[i] = max; } int ret = 0; - for(int i = 0; i < n; i++){ + for (int i = 0; i < n; i++) { ret = Math.max(ret, dp[i]); } return ret; } ``` -以上解法的时间复杂度为 O(n2) ,可以使用二分查找使得时间复杂度降低为 O(nlogn)。定义一个 tails 数组,其中 tails[i] 存储长度为 i + 1 的最长递增子序列的最后一个元素,例如对于数组 [4,5,6,3],有 +以上解法的时间复杂度为 O(N2) ,可以使用二分查找将时间复杂度降低为 O(NlogN)。定义一个 tails 数组,其中 tails[i] 存储长度为 i + 1 的最长递增子序列的最后一个元素。如果有多个长度相等的最长递增子序列,那么 tails[i] 就取最小值。例如对于数组 [4,5,6,3],有 ```html len = 1 : [4], [5], [6], [3] => tails[0] = 3 @@ -1973,6 +1974,7 @@ len = 2 : [4, 5], [5, 6] => tails[1] = 5 len = 3 : [4, 5, 6] => tails[2] = 6 ``` + 对于一个元素 x, - 如果它大于 tails 数组所有的值,那么把它添加到 tails 后面,表示最长递增子序列长度加 1; @@ -2094,7 +2096,7 @@ public int wiggleMaxLength(int[] nums) { - 针对的是两个序列,求它们的最长公共子序列。 - 在最长递增子序列中,dp[i] 表示以 Si 为结尾的最长递增子序列长度,子序列必须包含 Si ;在最长公共子序列中,dp[i][j] 表示 S1 中前 i 个字符与 S2 中前 j 个字符的最长公共子序列长度,不一定包含 S1i 和 S2j 。 -- 由于 2 ,在求最终解时,最长公共子序列中 dp[N][M] 就是最终解,而最长递增子序列中 dp[N] 不是最终解,因为以 SN 为结尾的最长递增子序列不一定是整个序列最长递增子序列,需要遍历一遍 dp 数组找到最大者。 +- 在求最终解时,最长公共子序列中 dp[N][M] 就是最终解,而最长递增子序列中 dp[N] 不是最终解,因为以 SN 为结尾的最长递增子序列不一定是整个序列最长递增子序列,需要遍历一遍 dp 数组找到最大者。 ```java public int lengthOfLCS(int[] nums1, int[] nums2) { @@ -2114,7 +2116,7 @@ public int lengthOfLCS(int[] nums1, int[] nums2) { 有一个容量为 N 的背包,要用这个背包装下物品的价值最大,这些物品有两个属性:体积 w 和价值 v。 -定义一个二维数组 dp 存储最大价值,其中 dp[i][j] 表示体积不超过 j 的情况下,前 i 件物品能达到的最大价值。设第 i 件物品体积为 w,价值为 v,根据第 i 件物品是否添加到背包中,可以分两种情况讨论: +定义一个二维数组 dp 存储最大价值,其中 dp[i][j] 表示前 i 件物品体积不超过 j 的情况下能达到的最大价值。设第 i 件物品体积为 w,价值为 v,根据第 i 件物品是否添加到背包中,可以分两种情况讨论: - 第 i 件物品没添加到背包,总体积不超过 j 的前 i 件物品的最大价值就是总体积不超过 j 的前 i-1 件物品的最大价值,dp[i][j] = dp[i-1][j]。 - 第 i 件物品添加到背包中,dp[i][j] = dp[i-1][j-w] + v。 @@ -2306,9 +2308,7 @@ public int findTargetSumWays(int[] nums, int S) { } private int findTargetSumWays(int[] nums, int start, int S) { - if (start == nums.length) { - return S == 0 ? 1 : 0; - } + if (start == nums.length) return S == 0 ? 1 : 0; return findTargetSumWays(nums, start + 1, S + nums[start]) + findTargetSumWays(nums, start + 1, S - nums[start]); } ``` @@ -2362,7 +2362,7 @@ return -1. 题目描述:给一些面额的硬币,要求用这些硬币来组成给定面额的钱数,并且使得硬币数量最少。硬币可以重复使用。 -这是一个完全背包问题。 +这是一个完全背包问题,完全背包问题和 0-1 背包问题在实现上的区别在于,0-1 背包遍历物品的循环在外侧,而完全背包问题遍历物品的循环在内侧,在内侧体现出物品可以使用多次。 ```java public int coinChange(int[] coins, int amount) { @@ -2371,7 +2371,7 @@ public int coinChange(int[] coins, int amount) { Arrays.fill(dp, amount + 1); dp[0] = 0; for (int i = 1; i <= amount; i++) { - for (int c : coins) { + for (int c : coins) { // 硬币可以使用多次 if (c <= i) { dp[i] = Math.min(dp[i], dp[i - c] + 1); } @@ -2584,10 +2584,31 @@ public int minDistance(String word1, String word2) { } ``` -**修改一个字符串称为另一个字符串** +**修改一个字符串成为另一个字符串** [Leetcode : 72. Edit Distance (Hard)](https://leetcode.com/problems/edit-distance/description/) +```html +Example 1: + +Input: word1 = "horse", word2 = "ros" +Output: 3 +Explanation: +horse -> rorse (replace 'h' with 'r') +rorse -> rose (remove 'r') +rose -> ros (remove 'e') +Example 2: + +Input: word1 = "intention", word2 = "execution" +Output: 5 +Explanation: +intention -> inention (remove 't') +inention -> enention (replace 'i' with 'e') +enention -> exention (replace 'n' with 'x') +exention -> exection (replace 'n' with 'c') +exection -> execution (insert 'u') +``` + ```java public int minDistance(String word1, String word2) { if (word1 == null || word2 == null) { @@ -2646,12 +2667,12 @@ public int numSquares(int n) { List squareList = generateSquareList(n); int[] dp = new int[n + 1]; for (int i = 1; i <= n; i++) { - int max = Integer.MAX_VALUE; + int min = Integer.MAX_VALUE; for (int square : squareList) { if (square > i) break; - max = Math.min(max, dp[i - square] + 1); + min = Math.min(min, dp[i - square] + 1); } - dp[i] = max; + dp[i] = min; } return dp[n]; } @@ -2767,7 +2788,7 @@ public int minPathSum(int[][] grid) { 题目描述:交易之后需要有一天的冷却时间。 -

+

```java public int maxProfit(int[] prices) { @@ -2806,7 +2827,7 @@ The total profit is ((8 - 1) - 2) + ((9 - 4) - 2) = 8. 题目描述:每交易一次,都要支付一定的费用。 -

+

```java public int maxProfit(int[] prices, int fee) { @@ -2833,7 +2854,7 @@ public int maxProfit(int[] prices, int fee) { 只进行一次交易。 -只要记录前面的最小价格,将这个最小价格作为买入价格,然后将当前的价格作为售出价格,查看这个价格是否是当前的最大价格。 +只要记录前面的最小价格,将这个最小价格作为买入价格,然后将当前的价格作为售出价格,查看当前收益是不是最大收益。 ```java public int maxProfit(int[] prices) { diff --git a/pics/61942711-45a0-4e11-bbc9-434e31436f33.png b/pics/61942711-45a0-4e11-bbc9-434e31436f33.png new file mode 100644 index 0000000000000000000000000000000000000000..8f093ef638851556ed2328d64557a726abfb0c92 GIT binary patch literal 27648 zcma&OWmJ@5*EXyoos!ZmC?!aDw}2opba#t%iF9`hNJ$MaG?G%%AdPglAl>kt<9$ER z`@a7^*J8P5<~nPiwfAux`wCN0lEFkHL3{M*5vH81r0SzbkEg)j3JMbVX51`W8T|Ly zSye{-QTZ_04*2lYLhP;Bqeqo7=(k3Q;4`YDtd{emM_8TkzsEfeMW&A)S?$P4ioJK& z|C5RAPN07M(=HSdk>D`_J^{KPsc~oU=T+A5a+D@!SCyxPv8&;T?~`-!#Uw?IcD_=d zzM%gU8>o**L-kmk;McD>n!vkz4~91h&JHj7T6mlfGt+MFv$njRSo8}yn{PHd4_^(t z4L=h{p$?ou3QEEeN2z><#zY;6V@!*$0lw{$W`qXfh=)e0Kv2QAq&YBL@GZBv5k2_U 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