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* Translate 1.0.0b6 release with the machine learning translator. * Update Dockerfile A few translation improvements. * Fix a badge logo. * Fix EN translation of chapter_appendix/terminology.md (#913) * Update README.md * Update README.md * translation: Refined the automated translation of README (#932) * refined the automated translation of README * Update index.md * Update mkdocs-en.yml --------- Co-authored-by: Yudong Jin <krahets@163.com> * translate: Embellish chapter_computational_complexity/index.md (#940) * translation: Update chapter_computational_complexity/performance_evaluation.md (#943) * Update performance_evaluation.md * Update performance_evaluation.md * Update performance_evaluation.md change 'methods' to 'approaches' on line 15 * Update performance_evaluation.md on line 21, change the sentence to 'the results could be the opposite on another computer with different specifications.' * Update performance_evaluation.md delete two short sentence on line 5 and 6 * Update performance_evaluation.md change `unavoidable` to `inevitable` on line 48 * Update performance_evaluation.md small changes on line 23 * translation: Update terminology and improve readability in preface summary (#954) * Update terminology and improve readability in preface summary This commit made a few adjustments in the 'summary.md' file for clearer and more accessible language. "Brushing tool library" was replaced with "Coding Toolkit" to better reflect common terminology. Also, advice for beginners in algorithm learning journey was reformulated to imply a more positive approach avoiding detours and common pitfalls. The section related to the discussion forum was rewritten to sound more inviting to readers. * Format * Optimize the translation of chapter_introduction/algorithms_are_everywhere. * Add .gitignore to Java subfolder. * Update the button assets. * Fix the callout * translation: chapter_computational_complexity/summary to en (#953) * translate chapter_computational_complexity/summary * minor format * Update summary.md with comment * Update summary.md * Update summary.md * translation: chapter_introduction/what_is_dsa.md (#962) * Optimize translation of what_is_dsa.md * Update * translation: chapter_introduction/summary.md (#963) * Translate chapter_introduction/summary.md * Update * translation: Update README.md (#964) * Update en translation of README.md * Update README.md * translation: update space_complexity.md (#970) * update space_complexity.md * the rest of translation piece * Update space_complexity.md --------- Co-authored-by: ThomasQiu <thomas.qiu@mnfgroup.limited> Co-authored-by: Yudong Jin <krahets@163.com> * translation: Update chapter_introduction/index.md (#971) * Update index.md sorry, first time doing this... now this is the final change. changes: title of the chapter is shorter. refined the abstract. * Update index.md --------- Co-authored-by: Yudong Jin <krahets@163.com> * translation: Update chapter_data_structure/classification_of_data_structure.md (#980) * update classification_of_data_structure.md * Update classification_of_data_structure.md --------- Co-authored-by: Yudong Jin <krahets@163.com> * translation: Update chapter_introduction/algorithms_are_everywhere.md (#972) * Update algorithms_are_everywhere.md changed or refined parts of the words and sentences including tips. Some of them I didnt change that much because im worried that it might not meet the requirement of accuracy. some other ones i changed a lot to make it sound better, but also kind of following the same wording as the CN version * Update algorithms_are_everywhere.md re-edited the dictionary part from Piyin to just normal Eng dictionary. again thank you very much hpstory for you suggestion. * Update algorithms_are_everywhere.md --------- Co-authored-by: Yudong Jin <krahets@163.com> * Prepare merging into main branch. * Update buttons * Update Dockerfile * Update index.md * Update index.md * Update README * Fix index.md * Fix mkdocs-en.yml --------- Co-authored-by: Yuelin Xin <sc20yx2@leeds.ac.uk> Co-authored-by: Phoenix Xie <phoenixx0415@gmail.com> Co-authored-by: Sizhuo Long <longsizhuo@gmail.com> Co-authored-by: Spark <qizhang94@outlook.com> Co-authored-by: Thomas <thomasqiu7@gmail.com> Co-authored-by: ThomasQiu <thomas.qiu@mnfgroup.limited> Co-authored-by: K3v123 <123932560+K3v123@users.noreply.github.com> Co-authored-by: Jin <36914748+yanedie@users.noreply.github.com>
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Classification Of Data Structures
Common data structures include arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs. They can be divided into two categories: logical structure and physical structure.
Logical Structures: Linear And Non-linear
Logical structures reveal logical relationships between data elements. In arrays and linked lists, data are arranged in sequential order, reflecting 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 complex network relationship.
As shown in the figure below, logical structures can further be divided into "linear data structure" and "non-linear data structure". Linear data structures are more intuitive, meaning that the data are arranged linearly in terms of logical relationships; non-linear data structures, on the other hand, are arranged non-linearly.
- Linear data structures: arrays, linked lists, stacks, queues, hash tables.
- Nonlinear data structures: trees, heaps, graphs, hash tables.
Non-linear data structures can be further divided into tree and graph structures.
- Linear structures: arrays, linked lists, queues, stacks, hash tables, with one-to-one sequential relationship between elements.
- Tree structure: tree, heap, hash table, with one-to-many relationship between elements.
- Graph: graph with many-to-many relationship between elements.
Physical Structure: Continuous vs. Dispersed
When an algorithm is running, the data being processed is stored in memory. The figure below shows a computer memory module where each black square represents a memory space. We can think of the memory as a giant Excel sheet in which each cell can store data of a certain size.
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 in memory.
!!! tip
It is worth noting that comparing memory to the Excel sheet is a simplified analogy. The actual memory working mechanism is more complicated, involving the concepts of address, space, memory management, cache mechanism, virtual and physical memory.
Memory is a shared resource for all programs, and when a block of memory is occupied by one program, it cannot be used by other programs at the same time. Therefore, considering memory resources is crucial in designing data structures and algorithms. For example, 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.
As shown in the figure below, Physical structure reflects the way data is stored in computer memory and it can be divided into consecutive space storage (arrays) and distributed space storage (linked lists). The physical structure determines how data is accessed, updated, added, deleted, etc. Logical and physical structure complement each other in terms of time efficiency and space efficiency.
It is worth stating that all data structures are implemented based on arrays, linked lists, or a combination of the two. For example, stacks and queues can be implemented using both arrays and linked lists; and implementations of hash tables may contain both arrays and linked lists.
- Array-based structures: stacks, queues, hash tables, trees, heaps, graphs, matrices, tensors (arrays of dimension
\geq 3), and so on. - Linked list-based structures: stacks, queues, hash tables, trees, heaps, graphs, etc.
Data structures based on arrays are also known as "static data structures", which means that such structures' length remains constant after initialization. In contrast, data structures based on linked lists are called "dynamic data structures", meaning that their length can be adjusted during program execution after initialization.
!!! tip
If you find it difficult to understand the physical structure, it is recommended that you read the next chapter, "Arrays and Linked Lists," before reviewing this section.