translation: update searching_algorithm_revisited.md #1559
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fix typo in original commit
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| Searching algorithms can be divided into the following two categories based on their implementation approaches. | ||
| Searching algorithms can be divided into the following two categories based on their approach. | ||
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'Implementation approaches" is redundant (it says the same thing twice). We could also say 'based on their implementation'.
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| - **Locating the target element by traversing the data structure**, such as traversals of arrays, linked lists, trees, and graphs, etc. | ||
| - **Using the organizational structure of the data or the prior information contained in the data to achieve efficient element search**, such as binary search, hash search, and binary search tree search, etc. | ||
| - **Using the organizational structure of the data or existing data to achieve efficient element searches**, such as binary search, hash search, binary search tree search, etc. | ||
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- 'prior information contained in the data' can be shortened. We could also say 'existing information' instead of 'existing data'.
- 'searches' or we could say 'an efficient element search'
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I would propose this version:
Efficient search may be achieved through the way the data is structured or the implicit prerequisite from the data structure, such as...
The benefits are:
- it is easy to understand
- by reversing the sentence structure, the "such as" clause is closer to the object it refers to
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Since the context is about the implementation, I think using a gerund here is better. This version may not be consistent with the former clause (Locating...). @thomasq0
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| It is not difficult to notice that these topics have been introduced in previous chapters, so searching algorithms are not unfamiliar to us. In this section, we will revisit searching algorithms from a more systematic perspective. | ||
| These topics were introduced in previous chapters, so they are not unfamiliar to us. In this section, we will revisit searching algorithms from a more systematic perspective. | ||
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- 'it is not difficult to notice' sounds strange in this context (almost pretentious). This is a style suggestion.
- replaced 'searching algorithms' with 'they' because we use it again in the next sentence so it starts to sound repetitive.
| - Not suitable for scenarios with frequent additions and deletions, because maintaining an ordered array incurs high overhead. | ||
| - Suitable for larger data volumes, with stable performance and a worst-case time complexity of $O(\log n)$. | ||
| - However, the data volume cannot be too large, because storing arrays requires contiguous memory space. | ||
| - Not suitable for scenarios with frequent additions and deletions, because maintaining an ordered array incurs a lot of overhead. | ||
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The first bullet point ('Suitable for large data volumes') seems to directly contradict the second bullet point ('The data volume cannot be too large') so it helps to clarify by adding 'However'.
(I imagine taking notes and seeing 'Suitable for large data volumes' so I write that down -> 'Good for large data'. Then I see 'The data volume cannot be too large' so I write that down -> 'Not good for large data'. With 'However' we clarify that there is a balance we need to be aware of.)
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| # Search algorithms revisited | |||
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| <u>Searching algorithms (searching algorithm)</u> are used to search for one or several elements that meet specific criteria in data structures such as arrays, linked lists, trees, or graphs. | |||
| <u>Searching algorithms (search algorithms)</u> are used to retrieve one or more elements that meet specific criteria within data structures such as arrays, linked lists, trees, or graphs. | |||
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- Keep consistency with the title - "Search algorithms revisited"
- We search for an element but then we need to do something with it - "retrieve"
- the "specific criteria" relates to the element, not the data structure - add 'within' to make this clearer
krahets
changed the title
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| - **Locating the target element by traversing the data structure**, such as traversals of arrays, linked lists, trees, and graphs, etc. | ||
| - **Using the organizational structure of the data or the prior information contained in the data to achieve efficient element search**, such as binary search, hash search, and binary search tree search, etc. | ||
| - **Using the organizational structure of the data or existing data to achieve efficient element searches**, such as binary search, hash search, binary search tree search, etc. | ||
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I would propose this version:
Efficient search may be achieved through the way the data is structured or the implicit prerequisite from the data structure, such as...
The benefits are:
- it is easy to understand
- by reversing the sentence structure, the "such as" clause is closer to the object it refers to
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| It is not difficult to notice that these topics have been introduced in previous chapters, so searching algorithms are not unfamiliar to us. In this section, we will revisit searching algorithms from a more systematic perspective. | ||
| These topics were introduced in previous chapters, so they are not unfamiliar to us. In this section, we will revisit searching algorithms from a more systematic perspective. |
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I would use "have been discussed" instead of "introduced", which may be paired with "idea".
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| However, **the time complexity of this type of algorithm is $O(n)$**, where $n$ is the number of elements, so the performance is poor in cases of large data volumes. | ||
| However, **the time complexity of this type of algorithm is $O(n)$**, where $n$ is the number of elements, so the performance is poor with large data sets. |
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It is not related to the translation. The performance is not
BTW, it should be
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It is not related to the translation. The performance is not O ( n ) for BFS and DFS.
BTW, it should be O ( n ) not 0 ( n ) . Other places may be updated too.
There is no need to update here since the markdown has not been converted to HTML and rendered by mathjax.js yet.
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| ## Adaptive search | ||
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| Adaptive search uses the unique properties of data (such as order) to optimize the search process, thereby locating the target element more efficiently. | ||
| An Adaptive search uses the unique properties of data (such as order) to optimize the search process, thereby locating the target element more efficiently. | ||
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| - "Binary search" uses the orderliness of data to achieve efficient searching, only suitable for arrays. |
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We may use "elements" instead of "data" here according to the context to avoid ambiguity. This may apply to the rest of the paragraphs.
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| ## Adaptive search | ||
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| Adaptive search uses the unique properties of data (such as order) to optimize the search process, thereby locating the target element more efficiently. | ||
| An Adaptive search uses the unique properties of data (such as order) to optimize the search process, thereby locating the target element more efficiently. | ||
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| - "Binary search" uses the orderliness of data to achieve efficient searching, only suitable for arrays. | ||
| - "Hash search" uses a hash table to establish a key-value mapping between search data and target data, thus implementing the query operation. |
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we may use "key" instead of "data" here. For example, "between search key and target entry"
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| ## Adaptive search | ||
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| Adaptive search uses the unique properties of data (such as order) to optimize the search process, thereby locating the target element more efficiently. | ||
| An Adaptive search uses the unique properties of data (such as order) to optimize the search process, thereby locating the target element more efficiently. | ||
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| - "Binary search" uses the orderliness of data to achieve efficient searching, only suitable for arrays. | ||
| - "Hash search" uses a hash table to establish a key-value mapping between search data and target data, thus implementing the query operation. | ||
| - "Tree search" in a specific tree structure (such as a binary search tree), quickly eliminates nodes based on node value comparisons, thus locating the target element. |
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"Tree search" may be used in certain tree structures, like binary search tree, to exclude nodes that do not meet the criteria, which leads to better performance.
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I think the last part "which leads to better performance" is unnecessary and the two sentences before it (line
27&28) should be changed as well to maintain consistency. Here is the context for your reference:
自适应搜索利用数据的特有属性（例如有序性）来优化搜索过程，从而更高效地定位目标元素。
“二分查找”利用数据的有序性实现高效查找，仅适用于数组。
“哈希查找”利用哈希表将搜索数据和目标数据建立为键值对映射，从而实现查询操作。
“树查找”在特定的树结构（例如二叉搜索树）中，基于比较节点值来快速排除节点，从而定位目标元素。
此类算法的优点是效率高 ......
You can see that the explanation focuses on how different search methods locate the target element, rather than on their performance. Therefore, performance is not the focus, and the other two sentences do not mention it either.
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| - "Binary search" uses the orderliness of data to achieve efficient searching, only suitable for arrays. | ||
| - "Hash search" uses a hash table to establish a key-value mapping between search data and target data, thus implementing the query operation. | ||
| - "Tree search" in a specific tree structure (such as a binary search tree), quickly eliminates nodes based on node value comparisons, thus locating the target element. | ||
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| The advantage of these algorithms is high efficiency, **with time complexities reaching $O(\log n)$ or even $O(1)$**. | ||
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| However, **using these algorithms often requires data preprocessing**. For example, binary search requires sorting the array in advance, and hash search and tree search both require the help of additional data structures, maintaining these structures also requires extra time and space overhead. | ||
| However, **using these algorithms often requires data preprocessing**. For example, binary search requires sorting the array in advance, and hash search and tree search both require the help of additional data structures. Maintaining these structures also requires more overhead in terms of time and space. |
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both hash search and tree search require ...
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| - "Binary search" uses the orderliness of data to achieve efficient searching, only suitable for arrays. | ||
| - "Hash search" uses a hash table to establish a key-value mapping between search data and target data, thus implementing the query operation. | ||
| - "Tree search" in a specific tree structure (such as a binary search tree), quickly eliminates nodes based on node value comparisons, thus locating the target element. | ||
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| The advantage of these algorithms is high efficiency, **with time complexities reaching $O(\log n)$ or even $O(1)$**. | ||
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| However, **using these algorithms often requires data preprocessing**. For example, binary search requires sorting the array in advance, and hash search and tree search both require the help of additional data structures, maintaining these structures also requires extra time and space overhead. | ||
| However, **using these algorithms often requires data preprocessing**. For example, binary search requires sorting the array in advance, and hash search and tree search both require the help of additional data structures. Maintaining these structures also requires more overhead in terms of time and space. | ||
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| !!! tip | ||
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| Adaptive search algorithms are often referred to as search algorithms, **mainly used for quickly retrieving target elements in specific data structures**. |
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referred to as "lookup algorithm", otherwise, it is confusing.
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