https://time-friend.com/en/tags/algorithms/Recent content in Algorithms on Time FriendHugoen-GBWed, 29 Jul 2026 15:15:07 +0800https://time-friend.com/en/archive/algorithm-complexity-analysis/Tue, 19 Dec 2017 22:17:36 +0000https://time-friend.com/en/archive/algorithm-complexity-analysis/<p>Everything is to count the efficiency of code execution</p> <h2 id="post-statistical-method">Post-Statistical Method</h2> <p>Through statistics and monitoring, we can get the execution time and memory usage of the algorithm</p> <h2 id="big-o-complexity-notation">Big O Complexity Notation</h2> <p>As data scale grows, algorithm execution time and space usage grow proportionally according to polynomials</p> <h3 id="time-complexity-ranking-from-best-to-worst">Time Complexity Ranking (from best to worst)</h3> <p><img src="https://incomparable9527.coding.net/p/imageBed/d/imageBed/git/raw/master/35125f99fc7c79d019449ecdf8ef04434feaef35ccc580fd22326dffe1e58ca6.png" alt="Figure 1"></p> <p>O(1) (constant order) &lt; O(logn) (logarithmic order) &lt; O(n) (linear order) &lt; O(nlogn) (linear logarithmic order) &lt; O(n^2) (quadratic order) &lt; O(n^3) (cubic order) &lt; O(2^n) (exponential order) &lt; O(n!) (factorial order)</p>