xinyin025/hello-algo
1
0
Fork 0
mirror of https://github.com/krahets/hello-algo.git synced 2026-07-07 05:04:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Revisit the English version (#1835)

Browse Source 
* Review the English version using Claude-4.5.

* Update mkdocs.yml

* Align the section titles.

* Bug fixes
This commit is contained in:
Yudong Jin
2025-12-30 17:54:01 +08:00
committed by GitHub
parent 091afd38b4
commit 45e1295241
106 changed files with 4195 additions and 3398 deletions
Download Patch File Download Diff File Expand all files Collapse all files
en/docs/chapter_data_structure/classification_of_data_structure.md
+21 -21
View File
@@ -1,48 +1,48 @@
# Classification of data structures
# Classification of Data Structures
Common data structures include arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs. They can be classified into "logical structure" and "physical structure".
Common data structures include arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs. They can be classified from two dimensions: "logical structure" and "physical structure".
## Logical structure: linear and non-linear
## Logical Structure: Linear and Non-Linear
**The logical structures reveal the logical relationships between data elements**. In arrays and linked lists, data are arranged in a specific sequence, demonstrating the linear relationship between data; while in trees, data are arranged hierarchically from the top down, showing the derived relationship between "ancestors" and "descendants"; and graphs are composed of nodes and edges, reflecting the intricate network relationship.
**Logical structure reveals the logical relationships between data elements**. In arrays and linked lists, data is arranged in a certain order, embodying the linear relationship between data; while in trees, data is arranged hierarchically from top to bottom, showing the derived relationship between "ancestors" and "descendants"; graphs are composed of nodes and edges, reflecting complex network relationships.
As shown in the figure below, logical structures can be divided into two major categories: "linear" and "non-linear". Linear structures are more intuitive, indicating data is arranged linearly in logical relationships; non-linear structures, conversely, are arranged non-linearly.
As shown in the figure below, logical structures can be divided into two major categories: "linear" and "non-linear". Linear structures are more intuitive, indicating that data is linearly arranged in logical relationships; non-linear structures are the opposite, arranged non-linearly.
- **Linear data structures**: Arrays, Linked Lists, Stacks, Queues, Hash Tables, where elements have a one-to-one sequential relationship.
- **Non-linear data structures**: Trees, Heaps, Graphs, Hash Tables.
- **Linear data structures**: Arrays, linked lists, stacks, queues, hash tables, where elements have a one-to-one sequential relationship.
- **Non-linear data structures**: Trees, heaps, graphs, hash tables.
Non-linear data structures can be further divided into tree structures and network structures.
- **Tree structures**: Trees, Heaps, Hash Tables, where elements have a one-to-many relationship.
- **Network structures**: Graphs, where elements have a many-to-many relationships.
- **Tree structures**: Trees, heaps, hash tables, where elements have a one-to-many relationship.
- **Network structures**: Graphs, where elements have a many-to-many relationship.
![Linear and non-linear data structures](classification_of_data_structure.assets/classification_logic_structure.png)
## Physical structure: contiguous and dispersed
## Physical Structure: Contiguous and Dispersed
**During the execution of an algorithm, the data being processed is stored in memory**. The figure below shows a computer memory stick where each black square is a physical memory space. We can think of memory as a vast Excel spreadsheet, with each cell capable of storing a certain amount of data.
**When an algorithm program runs, the data being processed is mainly stored in memory**. The figure below shows a computer memory stick, where each black square contains a memory space. We can imagine memory as a huge Excel spreadsheet, where each cell can store a certain amount of data.
**The system accesses the data at the target location by means of a memory address**. As shown in the figure below, the computer assigns a unique identifier to each cell in the table according to specific rules, ensuring that each memory space has a unique memory address. With these addresses, the program can access the data stored in memory.
**The system accesses data at the target location through memory addresses**. As shown in the figure below, the computer assigns a number to each cell in the spreadsheet according to specific rules, ensuring that each memory space has a unique memory address. With these addresses, the program can access data in memory.
![Memory stick, memory spaces, memory addresses](classification_of_data_structure.assets/computer_memory_location.png)
![Memory stick, memory space, memory address](classification_of_data_structure.assets/computer_memory_location.png)
!!! tip
It's worth noting that comparing memory to an Excel spreadsheet is a simplified analogy. The actual working mechanism of memory is more complex, involving concepts like address space, memory management, cache mechanisms, virtual memory, and physical memory.
It is worth noting that comparing memory to an Excel spreadsheet is a simplified analogy. The actual working mechanism of memory is quite complex, involving concepts such as address space, memory management, cache mechanisms, virtual memory, and physical memory.
Memory is a shared resource for all programs. When a block of memory is occupied by one program, it cannot be simultaneously used by other programs. **Therefore, memory resources are an important consideration in the design of data structures and algorithms**. For instance, the algorithm's peak memory usage should not exceed the remaining free memory of the system; if there is a lack of contiguous memory blocks, then the data structure chosen must be able to be stored in non-contiguous memory blocks.
Memory is a shared resource for all programs. When a block of memory is occupied by a program, it usually cannot be used by other programs at the same time. **Therefore, in the design of data structures and algorithms, memory resources are an important consideration**. For example, the peak memory occupied by an algorithm should not exceed the remaining free memory of the system; if there is a lack of contiguous large memory blocks, then the data structure chosen must be able to be stored in dispersed memory spaces.
As illustrated in the figure below, **the physical structure reflects the way data is stored in computer memory** and it can be divided into contiguous space storage (arrays) and non-contiguous space storage (linked lists). The two types of physical structures exhibit complementary characteristics in terms of time efficiency and space efficiency.
As shown in the figure below, **physical structure reflects the way data is stored in computer memory**, and can be divided into contiguous space storage (arrays) and dispersed space storage (linked lists). The two physical structures exhibit complementary characteristics in terms of time efficiency and space efficiency.
![Contiguous space storage and dispersed space storage](classification_of_data_structure.assets/classification_phisical_structure.png)
**It is worth noting that all data structures are implemented based on arrays, linked lists, or a combination of both**. For example, stacks and queues can be implemented using either arrays or linked lists; while implementations of hash tables may involve both arrays and linked lists.
It is worth noting that **all data structures are implemented based on arrays, linked lists, or a combination of both**. For example, stacks and queues can be implemented using either arrays or linked lists; while the implementation of hash tables may include both arrays and linked lists.
- **Array-based implementations**: Stacks, Queues, Hash Tables, Trees, Heaps, Graphs, Matrices, Tensors (arrays with dimensions $\geq 3$).
- **Linked-list-based implementations**: Stacks, Queues, Hash Tables, Trees, Heaps, Graphs, etc.
- **Can be implemented based on arrays**: Stacks, queues, hash tables, trees, heaps, graphs, matrices, tensors (arrays with dimensions $\geq 3$), etc.
- **Can be implemented based on linked lists**: Stacks, queues, hash tables, trees, heaps, graphs, etc.
Data structures implemented based on arrays are also called “Static Data Structures,” meaning their length cannot be changed after initialization. Conversely, those based on linked lists are called “Dynamic Data Structures,” which can still adjust their size during program execution.
After initialization, linked lists can still adjust their length during program execution, so they are also called "dynamic data structures". After initialization, the length of arrays cannot be changed, so they are also called "static data structures". It is worth noting that arrays can achieve length changes by reallocating memory, thus possessing a certain degree of "dynamism".
!!! tip
If you find it challenging to comprehend the physical structure, it is recommended that you read the next chapter, "Arrays and Linked Lists," and revisit this section later.
If you find it difficult to understand physical structure, it is recommended to read the next chapter first, and then review this section.