Summarize problem-solving ideas and summarize knowledge points

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Consolidate theoretical knowledge: Transform abstract theoretical concepts into concrete programming practices.
Improve programming skills: Improve code quality and efficiency through repeated practice.
Cultivate algorithmic thinking: Choose Email List appropriate data structures and algorithms for different problems.
Discover knowledge loopholes: Find out the deficiencies in your knowledge by doing questions.
How to use exercise sets effectively?


Step by step: Follow the chapter order in the textbook and practice one by one.
Think independently: When you encounter a difficult problem, think independently first and don't rush to see the answer.
Hands-on practice: Convert ideas into code and debug and run.
Compare answers: Compare the similarities and differences between your own solution and the answer, and analyze the advantages and disadvantages.
Summary: Summarize problem-solving ideas and summarize knowledge points.
Recommended exercise sets and online resources



Exercises for supporting textbooks: Textbooks such as "Data Structure" edited by Yan Weimin and Wu Weimin are equipped with a wealth of exercises.
Online programming platforms: LeetCode, Codewars and other platforms provide a large number of algorithm questions, which can be filtered by difficulty and type.
Open source projects: There are many open source algorithm implementations on GitHub, which you can refer to and learn from.
Common types of exercises


Algorithm design question: Asked to design an algorithm to solve a given problem.
Code fill-in-the-blank question: A part of the code is given and asked to fill in the blanks.
Algorithm analysis question: It is required to analyze the time complexity and space complexity of the algorithm.
Proof question: It is required to prove the correctness of the algorithm.
Common test points for data structures
Linear list: array, linked list, stack, queue