You can use the ProcessExit event of the AppDomain: class Program { static void Main(string[] args) { AppDomain.CurrentDomain.ProcessExit += new EventHandler(CurrentDomain_ProcessExit); // do some work } static void CurrentDomain_ProcessExit(object sender, EventArgs e) { Console.WriteLine(“exit”); } } Update Here is a full example program with an empty “message pump” running on a separate thread, that allows … Read more
The time complexity, in Big O notation, for each function: int recursiveFun1(int n) { if (n <= 0) return 1; else return 1 + recursiveFun1(n-1); } This function is being called recursively n times before reaching the base case so its O(n), often called linear. int recursiveFun2(int n) { if (n <= 0) return 1; … Read more
It’s O(1) (constant time, not depending of actual length of the element – very fast) on every type you’ve mentioned, plus set and others such as array.array.
It depends on many things. It’s usually O(1), with a decent hash which itself is constant time… but you could have a hash which takes a long time to compute, and if there are multiple items in the hash map which return the same hash code, get will have to iterate over them calling equals … Read more
Yes, nested loops are one way to quickly get a big O notation. Typically (but not always) one loop nested in another will cause O(n²). Think about it, the inner loop is executed i times, for each value of i. The outer loop is executed n times. thus you see a pattern of execution like … Read more
Amortised time explained in simple terms: If you do an operation say a million times, you don’t really care about the worst-case or the best-case of that operation – what you care about is how much time is taken in total when you repeat the operation a million times. So it doesn’t matter if the … Read more
You model the time function to calculate Fib(n) as sum of time to calculate Fib(n-1) plus the time to calculate Fib(n-2) plus the time to add them together (O(1)). This is assuming that repeated evaluations of the same Fib(n) take the same time – i.e. no memoization is used. T(n<=1) = O(1) T(n) = T(n-1) … Read more
How to find time complexity of an algorithm You add up how many machine instructions it will execute as a function of the size of its input, and then simplify the expression to the largest (when N is very large) term and can include any simplifying constant factor. For example, lets see how we simplify … Read more
I’ll do my best to explain it here on simple terms, but be warned that this topic takes my students a couple of months to finally grasp. You can find more information on the Chapter 2 of the Data Structures and Algorithms in Java book. There is no mechanical procedure that can be used to … Read more
What is a plain English explanation of “Big O” notation?