Binary Search: Applicable Scenarios and Learning Guide for Beginners
This article introduces the binary search algorithm, whose core is to compare the middle element in an ordered array to gradually narrow down the search range and quickly locate the target. It is suitable for scenarios with ordered data, large data volumes, static (rarely modified) content, and the need for rapid search, such as dictionaries or configuration files. The search process uses left and right pointers to determine the middle value mid. Depending on the size of the target relative to the middle value, the pointers are adjusted: if the middle value equals the target, the search is successful; if the target is larger, left is moved right; if smaller, right is moved left, until the target is found or the range is invalid. The core code of the Python iterative implementation uses a loop with left <= right, calculates mid = (left + right) // 2, and handles boundaries to return -1 when the array is empty or the target does not exist. The time complexity is O(log n) (since the range is halved each time), and the space complexity is O(1) (using only constant variables). Key details include expanding the traversal when handling duplicate elements, directly judging single-element arrays, and returning -1 if the target is not found. The "divide and conquer" (reduction and governance) idea of binary search efficiently solves the problem of fast searching in ordered large datasets, making it an important tool in basic algorithms.
Read MoreSearch Algorithms: Differences Between Sequential Search and Binary Search, and Which Is Faster?
The article introduces two basic search algorithms: sequential search and binary search, which are used to locate specific elements in data. Sequential search (linear search) works by comparing elements one by one. It does not require the data to be ordered, with a time complexity of O(n) (where n is the amount of data). Its advantage is simplicity, but its drawback is low efficiency, making it suitable for small data volumes or unordered data. Binary search (half-interval search) requires the data to be sorted. It narrows down the search range by half through comparison, with a time complexity of O(log n). It is highly efficient (e.g., only about 10 comparisons needed when n=1000), but it requires handling boundary conditions, and is suitable for large-sized ordered data. Comparison of the two: Sequential search does not require data ordering and is simple to implement but inefficient; binary search requires ordering and has higher complexity but is faster. The choice depends on data size and ordering: binary search for large ordered data and sequential search for small unordered data.
Read MoreBinary Search: How Much Faster Than Linear Search? Search Techniques in Data Structures
This article introduces search algorithms in computers, focusing on linear search and binary search. Linear search (sequential search) is a basic method that checks data one by one from the beginning to the end. It has a time complexity of O(n) and is suitable for scenarios with small data volumes or unordered data. In the worst case, it needs to traverse all data. Binary search, on the other hand, requires an ordered array. Its core is to eliminate half of the data each time, with a time complexity of O(log n). When the data volume is large, it is far more efficient than linear search (e.g., for n=1 million, binary search only needs 20 times, while linear search needs 1 million times). The two have different applicable scenarios: binary search is suitable for ordered, large - data - volume, and frequently searched scenarios; linear search is suitable for unordered, small - data - volume, or dynamically changing data. In summary, binary search significantly improves efficiency through "half - by - half elimination" and is an efficient choice for large - volume ordered data. Linear search is more flexible in scenarios with small data volumes or unordered data.
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