数组遍历是数据结构和算法(DSA)中的一个基本概念,每个开发人员都应该掌握。在本综合指南中,我们将探索在 JavaScript 中遍历数组的各种技术,从基本方法开始,逐步发展到更高级的方法。我们将涵盖 20 个示例,范围从简单到高级,并包括 LeetCode 风格的问题来强化您的学习。
数组遍历是访问数组中的每个元素来执行某种操作的过程。这是编程中的一项关键技能,构成了许多算法和数据操作的基础。在 JavaScript 中,数组是通用的数据结构,提供多种方式来遍历和操作数据。
我们先从数组遍历的基本方法开始。
经典的 for 循环是遍历数组的最常见方法之一。
function sumArray(arr) { let sum = 0; for (let i = 0; i <p><strong>时间复杂度</strong>:O(n),其中n是数组的长度。</p> <p></p> <h3> 示例 2:使用 while 循环 </h3> <p>while循环也可以用于数组遍历,特别是当终止条件比较复杂时。<br> </p> <pre class="brush:php;toolbar:false">function findFirstNegative(arr) { let i = 0; while (i = 0) { i++; } return i <p><strong>时间复杂度</strong>:最坏情况下为 O(n),但如果尽早发现负数,时间复杂度可能会更低。</p> <p></p> <h3> 示例 3:使用 do-while 循环 </h3> <p>do-while 循环对于数组遍历不太常见,但在某些情况下很有用。<br> </p> <pre class="brush:php;toolbar:false">function printReverseUntilZero(arr) { let i = arr.length - 1; do { console.log(arr[i]); i--; } while (i >= 0 && arr[i] !== 0); } const numbers = [1, 3, 0, 5, 7]; printReverseUntilZero(numbers); // Output: 7, 5
时间复杂度:最坏情况下为 O(n),但如果尽早遇到零,时间复杂度可能会更低。
逆序遍历数组是许多算法中的常见操作。
function reverseTraversal(arr) { const result = []; for (let i = arr.length - 1; i >= 0; i--) { result.push(arr[i]); } return result; } const numbers = [1, 2, 3, 4, 5]; console.log(reverseTraversal(numbers)); // Output: [5, 4, 3, 2, 1]
时间复杂度:O(n),其中n是数组的长度。
ES6 和更高版本的 JavaScript 引入了强大的数组方法,可以简化遍历和操作。
forEach 方法提供了一种迭代数组元素的简洁方法。
function logEvenNumbers(arr) { arr.forEach(num => { if (num % 2 === 0) { console.log(num); } }); } const numbers = [1, 2, 3, 4, 5, 6]; logEvenNumbers(numbers); // Output: 2, 4, 6
时间复杂度:O(n),其中n是数组的长度。
map 方法创建一个新数组,其中包含对每个元素调用提供的函数的结果。
function doubleNumbers(arr) { return arr.map(num => num * 2); } const numbers = [1, 2, 3, 4, 5]; console.log(doubleNumbers(numbers)); // Output: [2, 4, 6, 8, 10]
时间复杂度:O(n),其中n是数组的长度。
filter 方法创建一个新数组,其中包含满足特定条件的所有元素。
function filterPrimes(arr) { function isPrime(num) { if (num <p><strong>时间复杂度</strong>:O(n * sqrt(m)),其中n是数组的长度,m是数组中最大的数字。</p> <p></p> <h3> 示例8:reduce方法 </h3> <p>reduce 方法将缩减函数应用于数组的每个元素,从而产生单个输出值。<br> </p> <pre class="brush:php;toolbar:false">function findMax(arr) { return arr.reduce((max, current) => Math.max(max, current), arr[0]); } const numbers = [3, 7, 2, 9, 1, 5]; console.log(findMax(numbers)); // Output: 9
时间复杂度:O(n),其中n是数组的长度。
现在让我们探索一些数组遍历的中间技术。
两指针技术通常用于高效解决数组相关问题。
function isPalindrome(arr) { let left = 0; let right = arr.length - 1; while (left <p><strong>Time Complexity</strong>: O(n/2) which simplifies to O(n), where n is the length of the array.</p> <p></p> <h3> Example 10: Sliding window </h3> <p>The sliding window technique is useful for solving problems involving subarrays or subsequences.<br> </p> <pre class="brush:php;toolbar:false">function maxSubarraySum(arr, k) { if (k > arr.length) return null; let maxSum = 0; let windowSum = 0; // Calculate sum of first window for (let i = 0; i <p><strong>Time Complexity</strong>: O(n), where n is the length of the array.</p> <p></p> <h3> Example 11: Kadane's Algorithm </h3> <p>Kadane's algorithm is used to find the maximum subarray sum in a one-dimensional array.<br> </p> <pre class="brush:php;toolbar:false">function maxSubarraySum(arr) { let maxSoFar = arr[0]; let maxEndingHere = arr[0]; for (let i = 1; i <p><strong>Time Complexity</strong>: O(n), where n is the length of the array.</p> <p></p> <h3> Example 12: Dutch National Flag Algorithm </h3> <p>This algorithm is used to sort an array containing three distinct elements.<br> </p> <pre class="brush:php;toolbar:false">function dutchFlagSort(arr) { let low = 0, mid = 0, high = arr.length - 1; while (mid <p><strong>Time Complexity</strong>: O(n), where n is the length of the array.</p> <p></p> <h2> 5. Advanced Traversal Techniques </h2> <p>Let's explore some more advanced techniques for array traversal.</p> <p></p> <h3> Example 13: Recursive traversal </h3> <p>Recursive traversal can be powerful for certain types of problems, especially those involving nested structures.<br> </p> <pre class="brush:php;toolbar:false">function sumNestedArray(arr) { let sum = 0; for (let element of arr) { if (Array.isArray(element)) { sum += sumNestedArray(element); } else { sum += element; } } return sum; } const nestedNumbers = [1, [2, 3], [[4, 5], 6]]; console.log(sumNestedArray(nestedNumbers)); // Output: 21
Time Complexity: O(n), where n is the total number of elements including nested ones.
Binary search is an efficient algorithm for searching a sorted array.
function binarySearch(arr, target) { let left = 0; let right = arr.length - 1; while (left <p><strong>Time Complexity</strong>: O(log n), where n is the length of the array.</p> <p></p> <h3> Example 15: Merge two sorted arrays </h3> <p>This technique is often used in merge sort and other algorithms.<br> </p> <pre class="brush:php;toolbar:false">function mergeSortedArrays(arr1, arr2) { const mergedArray = []; let i = 0, j = 0; while (i <p><strong>Time Complexity</strong>: O(n + m), where n and m are the lengths of the input arrays.</p> <p></p> <h3> Example 16: Quick Select Algorithm </h3> <p>Quick Select is used to find the kth smallest element in an unsorted array.<br> </p> <pre class="brush:php;toolbar:false">function quickSelect(arr, k) { if (k arr.length) { return null; } function partition(low, high) { const pivot = arr[high]; let i = low - 1; for (let j = low; j k - 1) { return select(low, pivotIndex - 1, k); } else { return select(pivotIndex + 1, high, k); } } return select(0, arr.length - 1, k); } const numbers = [3, 2, 1, 5, 6, 4]; console.log(quickSelect(numbers, 2)); // Output: 2 (2nd smallest element)
Time Complexity: Average case O(n), worst case O(n^2), where n is the length of the array.
Some scenarios require specialized traversal techniques, especially when dealing with multi-dimensional arrays.
Traversing 2D arrays (matrices) is a common operation in many algorithms.
function traverse2DArray(matrix) { const result = []; for (let i = 0; i <p><strong>Time Complexity</strong>: O(m * n), where m is the number of rows and n is the number of columns in the matrix.</p> <p></p> <h3> Example 18: Spiral Matrix Traversal </h3> <p>Spiral traversal is a more complex pattern often used in coding interviews and specific algorithms.<br> </p> <pre class="brush:php;toolbar:false">function spiralTraversal(matrix) { const result = []; if (matrix.length === 0) return result; let top = 0, bottom = matrix.length - 1; let left = 0, right = matrix[0].length - 1; while (top = left; i--) { result.push(matrix[bottom][i]); } bottom--; } if (left = top; i--) { result.push(matrix[i][left]); } left++; } } return result; } const matrix = [ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ]; console.log(spiralTraversal(matrix)); // Output: [1, 2, 3, 4, 8, 12, 11, 10, 9, 5, 6, 7]
Time Complexity: O(m * n), where m is the number of rows and n is the number of columns in the matrix.
Diagonal traversal of a matrix is another interesting pattern.
function diagonalTraversal(matrix) { const m = matrix.length; const n = matrix[0].length; const result = []; for (let d = 0; d = 0 && j <p><strong>Time Complexity</strong>: O(m * n), where m is the number of rows and n is the number of columns in the matrix.</p> <p></p> <h3> Example 20: Zigzag Traversal </h3> <p>Zigzag traversal is a pattern where we traverse the array in a zigzag manner.<br> </p> <pre class="brush:php;toolbar:false">function zigzagTraversal(matrix) { const m = matrix.length; const n = matrix[0].length; const result = []; let row = 0, col = 0; let goingDown = true; for (let i = 0; i <p><strong>Time Complexity</strong>: O(m * n), where m is the number of rows and n is the number of columns in the matrix.</p> <p></p> <h2> 7. Performance Considerations </h2> <p>When working with array traversals, it's important to consider performance implications:</p> <ol> <li><p><strong>Time Complexity</strong>: Most basic traversals have O(n) time complexity, where n is the number of elements. However, nested loops or recursive calls can increase this to O(n^2) or higher.</p></li> <li><p><strong>Space Complexity</strong>: Methods like map and filter create new arrays, potentially doubling memory usage. In-place algorithms are more memory-efficient.</p></li> <li><p><strong>Iterator Methods vs. For Loops</strong>: Modern methods like forEach, map, and filter are generally slower than traditional for loops but offer cleaner, more readable code.</p></li> <li><p><strong>Early Termination</strong>: for and while loops allow for early termination, which can be more efficient when you're searching for a specific element.</p></li> <li><p><strong>Large Arrays</strong>: For very large arrays, consider using for loops for better performance, especially if you need to break the loop early.</p></li> <li><p><strong>Caching Array Length</strong>: In performance-critical situations, caching the array length in a variable before the loop can provide a slight speed improvement.</p></li> <li><p><strong>Avoiding Array Resizing</strong>: When building an array dynamically, initializing it with a predetermined size (if possible) can improve performance by avoiding multiple array resizing operations.</p></li> </ol> <p></p><h2> 8.LeetCode练习题 </h2> <p>为了进一步加深您对数组遍历技术的理解,您可以练习以下 15 个 LeetCode 问题:</p> <ol> <li>两和</li> <li>买卖股票的最佳时机</li> <li>包含重复</li> <li>除自身之外的数组的乘积</li> <li>最大子数组</li> <li>移动零</li> <li>3Sum</li> <li>装最多水的容器</li> <li>旋转数组</li> <li>查找旋转排序数组中的最小值</li> <li>在旋转排序数组中搜索</li> <li>合并间隔</li> <li>螺旋矩阵</li> <li>设置矩阵零</li> <li>最长连续序列</li> </ol> <p>这些问题涵盖了广泛的数组遍历技术,并将帮助您应用我们在本博文中讨论的概念。</p> <p></p> <h2> 9. 结论 </h2> <p>数组遍历是编程中的一项基本技能,它构成了许多算法和数据操作的基础。从基本的 for 循环到滑动窗口和专门的矩阵遍历等高级技术,掌握这些方法将显着增强您高效解决复杂问题的能力。</p> <p>正如您通过这 20 个示例所看到的,JavaScript 提供了一组丰富的数组遍历工具,每个工具都有自己的优势和用例。通过了解何时以及如何应用每种技术,您将有能力应对各种编程挑战。</p> <p>记住,熟练的关键是练习。尝试在您自己的项目中实现这些遍历方法,当您对基础知识越来越熟悉时,请毫不犹豫地探索更高级的技术。提供的 LeetCode 问题将为您提供充足的机会在各种场景中应用这些概念。</p> <p>当您继续发展自己的技能时,请始终牢记您选择的遍历方法对性能的影响。有时,简单的 for 循环可能是最有效的解决方案,而在其他情况下,更专业的技术(例如滑动窗口或两指针方法)可能是最佳的。</p> <p>祝您编码愉快,愿您的数组始终能够高效地遍历!</p>
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