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@@ -1820,9 +1820,9 @@ Quadratic order is common in matrices and graphs, where the number of elements i
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/* 平方阶 */
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fun quadratic(n: Int) {
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// 矩阵占用 O(n^2) 空间
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val numMatrix: Array<Array<Int>?> = arrayOfNulls(n)
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val numMatrix = arrayOfNulls<Array<Int>?>(n)
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// 二维列表占用 O(n^2) 空间
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val numList: MutableList<MutableList<Int>> = arrayListOf()
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val numList = mutableListOf<MutableList<Int>>()
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for (i in 0..<n) {
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val tmp = mutableListOf<Int>()
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for (j in 0..<n) {
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@@ -1133,7 +1133,7 @@ Constant order means the number of operations is independent of the input data s
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/* 常数阶 */
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fun constant(n: Int): Int {
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var count = 0
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val size = 10_0000
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val size = 100000
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for (i in 0..<size)
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count++
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return count
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@@ -2056,7 +2056,7 @@ For instance, in bubble sort, the outer loop runs $n - 1$ times, and the inner l
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```kotlin title="time_complexity.kt"
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/* 平方阶(冒泡排序) */
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fun bubbleSort(nums: IntArray): Int {
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var count = 0
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var count = 0 // 计数器
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// 外循环:未排序区间为 [0, i]
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for (i in nums.size - 1 downTo 1) {
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// 内循环:将未排序区间 [0, i] 中的最大元素交换至该区间的最右端
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@@ -3119,7 +3119,7 @@ Linear-logarithmic order often appears in nested loops, with the complexities of
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if (n <= 1)
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return 1
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var count = linearLogRecur(n / 2) + linearLogRecur(n / 2)
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for (i in 0..<n.toInt()) {
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for (i in 0..<n) {
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count++
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}
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return count
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@@ -3747,10 +3747,9 @@ The "worst-case time complexity" corresponds to the asymptotic upper bound, deno
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for (i in 0..<n) {
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nums[i] = i + 1
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}
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// 随机打乱数组元素
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val mutableList = nums.toMutableList()
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// 随机打乱数组元素
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mutableList.shuffle()
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// Integer[] -> int[]
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val res = arrayOfNulls<Int>(n)
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for (i in 0..<n) {
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res[i] = mutableList[i]
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