### arcanae's blog

By arcanae, 4 years ago, , First of all, I apologize for my bad English :(

Are there any special reasons why the contests held in weekdays in November are all on Wednesdays and Fridays? I am only not free on Wednesdays and Fridays, so I have to skip these contests which is kind of frustrating. (The contests on weekends are fine for me.) I know that Round #332 was originally on Thursday and was postponed because of clashing with 101 Hack Nov, but I just want to suggest planning the contests in advance so this kind of incident won't happen. In the past months, the contests are always held on a different day each week (there are contests held on Mondays, Tuesdays and Thursdays). Can admins bear this in mind when planning the contests in the future? Many thanks :) I really want to participate in contests more frequently (not just in weekends)!!

May I also suggest publishing the contest times for the following month before it, ie. announce all the times and durations of each contest of December in the last week of November. I think this will avoid clashing with contests on other sites, and we can organize our own events beforehand.

Thanks for spending time to read my post. I look forward to the next contest! Unfortunately I'll have to miss the next two rounds, which means at least 2 weeks without contests :( By arcanae, 4 years ago, , By looking at the constraints of a problem, we can often "guess" the solution.

Common time complexities

Let n be the main variable in the problem.

• If n ≤ 12, the time complexity can be O(n!).
• If n ≤ 25, the time complexity can be O(2n).
• If n ≤ 100, the time complexity can be O(n4).
• If n ≤ 500, the time complexity can be O(n3).
• If n ≤ 104, the time complexity can be O(n2).
• If n ≤ 106, the time complexity can be O(n log n).
• If n ≤ 108, the time complexity can be O(n).
• If n > 108, the time complexity can be O(log n) or O(1).

Examples of each common time complexity

• O(n!) [Factorial time]: Permutations of 1 ... n
• O(2n) [Exponential time]: Exhaust all subsets of an array of size n
• O(n3) [Cubic time]: Exhaust all triangles with side length less than n
• O(n2) [Quadratic time]: Slow comparison-based sorting (eg. Bubble Sort, Insertion Sort, Selection Sort)
• O(n log n) [Linearithmic time]: Fast comparison-based sorting (eg. Merge Sort)
• O(n) [Linear time]: Linear Search (Finding maximum/minimum element in a 1D array), Counting Sort
• O(log n) [Logarithmic time]: Binary Search, finding GCD (Greatest Common Divisor) using Euclidean Algorithm
• O(1) [Constant time]: Calculation (eg. Solving linear equations in one unknown)

Explanations based on Codeforces problems

1. 255D Mr. Bender and Square
Observe that 1 ≤   n, c  ≤ 109. Referring to the information above, the program's time complexity should be either O(log n) or O(1). Since no O(1) solution exists, we conclude that binary search must be used.

2. 580B Kefa and Company
In this problem, 1 ≤  n  ≤ 105, which suggests that the time complexity can be either O(n log n) or O(n). It is quite obvious that sorting is required. Therefore, O(n log n) is the correct solution of this problem. 