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Kotlin 安卓算法优化指南

排序算法优化

1. 快速排序

// 使用三向切分的快速排序，对包含大量重复元素的数组更高效
fun optimizedQuickSort(arr: IntArray, low: Int = 0, high: Int = arr.lastIndex) {if (high <= low) returnvar lt = lowvar gt = highval pivot = arr[low]var i = low + 1while (i <= gt) {when {arr[i] < pivot -> arr.swap(lt++, i++)arr[i] > pivot -> arr.swap(i, gt--)else -> i++}}optimizedQuickSort(arr, low, lt - 1)optimizedQuickSort(arr, gt + 1, high)
}

2. 插入排序优化小数组

// 对小数组使用插入排序更高效
fun hybridSort(arr: IntArray, threshold: Int = 15) {if (arr.size <= threshold) {insertionSort(arr)} else {quickSort(arr)}
}private fun insertionSort(arr: IntArray) {for (i in 1 until arr.size) {val key = arr[i]var j = i - 1while (j >= 0 && arr[j] > key) {arr[j + 1] = arr[j]j--}arr[j + 1] = key}
}

搜索算法优化

1. 二分查找

// 使用插值查找优化均匀分布数据的查找
fun interpolationSearch(arr: IntArray, target: Int): Int {var low = 0var high = arr.size - 1while (low <= high && target >= arr[low] && target <= arr[high]) {val pos = low + ((target - arr[low]) * (high - low)) / (arr[high] - arr[low])when {arr[pos] == target -> return posarr[pos] < target -> low = pos + 1else -> high = pos - 1}}return -1
}

2. 优化的BFS

// 添加层级信息和提前终止条件的BFS
fun optimizedBfs(graph: Map<Int, List<Int>>, start: Int, target: Int? = null,maxLevel: Int = Int.MAX_VALUE
): Pair<List<Int>, Map<Int, Int>> {val visited = mutableListOf<Int>()val levels = mutableMapOf<Int, Int>().apply { put(start, 0) }val queue = ArrayDeque<Int>().apply { add(start) }while (queue.isNotEmpty()) {val node = queue.removeFirst()val currentLevel = levels[node] ?: 0if (currentLevel > maxLevel) breakif (node !in visited) {visited.add(node)if (node == target) breakgraph[node]?.forEach { neighbor ->if (neighbor !in levels) {levels[neighbor] = currentLevel + 1queue.add(neighbor)}}}}return Pair(visited, levels)
}

数据结构算法优化

1. 链表反转

// 尾递归优化的链表反转
fun tailRecReverseList(head: ListNode?): ListNode? {tailrec fun reverse(prev: ListNode?, current: ListNode?): ListNode? {if (current == null) return prevval next = current.nextcurrent.next = prevreturn reverse(current, next)}return reverse(null, head)
}

2. 树遍历

// 迭代式树遍历，避免递归栈溢出
fun iterativeInorderTraversal(root: TreeNode?): List<Int> {val result = mutableListOf<Int>()val stack = ArrayDeque<TreeNode>()var current = rootwhile (current != null || stack.isNotEmpty()) {while (current != null) {stack.push(current)current = current.left}current = stack.pop()result.add(current.`val`)current = current.right}return result
}

动态规划优化

1. 空间优化的斐波那契

// 使用矩阵快速幂将时间复杂度降至O(log n)
fun matrixFib(n: Int): Int {if (n <= 1) return nfun multiply(a: Array<IntArray>, b: Array<IntArray>): Array<IntArray> {return Array(2) { i ->IntArray(2) { j ->a[i][0] * b[0][j] + a[i][1] * b[1][j]}}}fun power(matrix: Array<IntArray>, n: Int): Array<IntArray> {if (n == 1) return matrixval half = power(matrix, n / 2)return if (n % 2 == 0) multiply(half, half) else multiply(multiply(half, half), matrix)}val matrix = arrayOf(intArrayOf(1, 1), intArrayOf(1, 0))val result = power(matrix, n - 1)return result[0][0]
}

2. 状态压缩的DP

// 使用滚动数组优化空间复杂度
fun optimizedKnapsack(weights: IntArray, values: IntArray, capacity: Int): Int {val dp = IntArray(capacity + 1)for (i in weights.indices) {for (w in capacity downTo weights[i]) {dp[w] = maxOf(dp[w], dp[w - weights[i]] + values[i])}}return dp[capacity]
}

安卓特有优化

1. 视图查找优化

// 使用缓存优化视图查找
fun findViewsWithType(root: View, type: Class<*>): List<View> {val result = mutableListOf<View>()val cache = mutableMapOf<Class<*>, Boolean>()fun isInstanceOf(view: View, clazz: Class<*>): Boolean {return cache.getOrPut(clazz) { clazz.isInstance(view) }}fun traverse(view: View) {if (isInstanceOf(view, type)) {result.add(view)}if (view is ViewGroup) {for (i in 0 until view.childCount) {traverse(view.getChildAt(i))}}}traverse(root)return result
}

2. 高效的防抖节流

// 结合防抖和节流的最佳实践
fun optimizedThrottle(delay: Long,leading: Boolean = false,trailing: Boolean = true,action: () -> Unit
): () -> Unit {var lastCallTime = 0Lvar timer: Job? = nullreturn {val currentTime = System.currentTimeMillis()val elapsed = currentTime - lastCallTimeif (elapsed >= delay) {if (leading) {action()lastCallTime = currentTime} else {timer?.cancel()timer = CoroutineScope(Dispatchers.Main).launch {delay(delay)if (trailing) {action()lastCallTime = System.currentTimeMillis()}}}} else {timer?.cancel()timer = CoroutineScope(Dispatchers.Main).launch {delay(delay - elapsed)if (trailing) {action()lastCallTime = System.currentTimeMillis()}}}}
}

集合处理优化

1. 并行处理大数据集

// 使用协程并行处理大数据集
suspend fun parallelProcess(data: List<DataItem>): List<Result> = coroutineScope {data.chunked(1000) // 分批处理.map { chunk ->async(Dispatchers.Default) {chunk.filter { it.isValid() }.map { transformItem(it) }}}.awaitAll().flatten()
}

2. 优化的集合差异查找

// 使用哈希集优化差异查找
fun <T> optimizedFindDifferences(oldList: List<T>,newList: List<T>,hash: (T) -> Int = { it.hashCode()