#timecomplexity — Public Fediverse posts

Big-O Notation học khám phá phức tạp thời gian trong code. Bài viết cung cấp kiến thức nền tảng về phân tích hiệu suất thuật toán (O(1), O(n), O(log n)...), hữu ích cho việc chuẩn bị coding interview. Kết thèm video và câu hỏi Reddit hỗ trợ tự học. #BigONotation #CodingInterview #TimeComplexity #ComputerScienceTutorial #VietnameseTechCommunity

https://www.reddit.com/r/programming/comments/1puq6nk/bigo_notation_everything_you_need_for_coding/

@benbenbrubaker @rrwilliams @tomgur @boazbaraktcs @joshuagrochow @ccanonne @manlius
Ooops!
Yet wouldn't doubt that also will appear on that list:

#AlgorithmicComplexity
#ComputationalComplexity
#TimeComplexity

@benbenbrubaker @rrwilliams @tomgur @boazbaraktcs @joshuagrochow @ccanonne @manlius
Ooops!
Yet wouldn't doubt that also will appear on that list:

#AlgorithmicComplexity
#ComputationalComplexity
#TimeComplexity

@benbenbrubaker @rrwilliams @tomgur @boazbaraktcs @joshuagrochow @ccanonne @manlius
Ooops!
Yet wouldn't doubt that also will appear on that list:

#AlgorithmicComplexity
#ComputationalComplexity
#TimeComplexity

@benbenbrubaker @rrwilliams @tomgur @boazbaraktcs @joshuagrochow @ccanonne @manlius
Ooops!
Yet wouldn't doubt that also will appear on that list:

#AlgorithmicComplexity
#ComputationalComplexity
#TimeComplexity

@benbenbrubaker @rrwilliams @tomgur @boazbaraktcs @joshuagrochow @ccanonne @manlius
Ooops!
Yet wouldn't doubt that also will appear on that list:

#AlgorithmicComplexity
#ComputationalComplexity
#TimeComplexity

Time Complexity (Big O) simplified:

- When your calculation is not dependent on the input size, it is a constant time complexity (O(1)).
- When the input size is reduced by half, maybe when iterating, handling recursion, or whatsoever, it is a logarithmic time complexity (O(log n)).
- When you have a single loop within your algorithm, it is linear time complexity (O(n)).
- When you have nested loops within your algorithm, meaning a loop in a loop, it is quadratic time complexity (O(n^2)).
- When the growth rate doubles with each addition to the input, it is exponential time complexity (O2^n).

#timecomplexity #BigO #programming #code #complexity

Time Complexity (Big O) simplified:

- When your calculation is not dependent on the input size, it is a constant time complexity (O(1)).
- When the input size is reduced by half, maybe when iterating, handling recursion, or whatsoever, it is a logarithmic time complexity (O(log n)).
- When you have a single loop within your algorithm, it is linear time complexity (O(n)).
- When you have nested loops within your algorithm, meaning a loop in a loop, it is quadratic time complexity (O(n^2)).
- When the growth rate doubles with each addition to the input, it is exponential time complexity (O2^n).

#timecomplexity #BigO #programming #code #complexity

Time Complexity (Big O) simplified:

- When your calculation is not dependent on the input size, it is a constant time complexity (O(1)).
- When the input size is reduced by half, maybe when iterating, handling recursion, or whatsoever, it is a logarithmic time complexity (O(log n)).
- When you have a single loop within your algorithm, it is linear time complexity (O(n)).
- When you have nested loops within your algorithm, meaning a loop in a loop, it is quadratic time complexity (O(n^2)).
- When the growth rate doubles with each addition to the input, it is exponential time complexity (O2^n).

#timecomplexity #BigO #programming #code #complexity