greedy

Here is the link of problem https://www.hackerrank.com/challenges/equal I read its editorial and unable to understand it. And if you are not make any account on hackerrank then surely you will not see it's editorial so here is some lines of editorial. This is equivalent to saying, christy can take away the chocolates of one coworker by 1, 2 or 5 while keeping others' chocolate untouched. Let's consider decreasing a coworker's chocolate as an operation. To minimize the number of operations, we should try to make the number of chocolates of every coworker equal to the minimum one in the group

How can I get all the matches in the following example: // Only "abcd" is matched MatchCollection greedyMatches = Regex.Matches("abcd", @"ab.*"); // Only "ab" is matched MatchCollection lazyMatches = Regex.Matches("abcd", @"ab.*?"); // How can I get all matches: "ab", "abc", "abcd" P.S.: I want to have the all matches in a generic manner. The example above is just an example. Tseng You could use something like: MatchCollection nonGreedyMatches = Regex.Matches("abcd", @"(((ab)c)d)"); Then you should have three backreferences with ab, abc and abcd. But, to be honest, this kind of regex doesn't

I came across this problem quite recently. Suppose there are n points on x-axis, x[0],x[1] .. x[n-1]. Let the weight associated with each of these points be w[0],w[1] .. w[n-1]. Starting from any point between 0 to n-1, the objective is to cover all the points such that the sum of w[i]*d[i] is minimized where d[i] is the distance covered to reach the ith point from the starting point. Example: Suppose the points are: 1 5 10 20 40 Suppose the weights are: 1 2 10 50 13 If I choose to start at point 10 and choose to move to point 20 then to 5 then to 40 and then finally to 1, then the weighted

In the book I am using Introduction to the Design & Analysis of Algorithms , dynamic programming is said to focus on the Principle of Optimality , "An optimal solution to any instance of an optimization problem is composed of optimal solutions to its subinstances". Whereas, the greedy technique focuses on expanding partially constructed solutions until you arrive at a solution for a complete problem. It is then said, it must be "the best local choice among all feasible choices available on that step". Since both involve local optimality, isn't one a subset of the other? Dynamic programming is

What is the main difference between dynamic programming and greedy approach in terms of usage? As far as I understood, the greedy approach sometimes gives an optimal solution; in other cases, the dynamic programming approach gives an optimal solution. Are there any particular conditions which must be met in order to use one approach (or the other) to obtain an optimal solution? Imposter Based on Wikipedia's articles. Greedy Approach A greedy algorithm is an algorithm that follows the problem solving heuristic of making the locally optimal choice at each stage with the hope of finding a global

I'm having trouble figuring out my last section of code for a Dynamic Coin Changing Problem. I have included the code below. I can't figure out the last else . Should I just use the greedy algorithm at that point or can I calculate the answer from values already in the table? I've worked hard on trying to understand this problem and I think I'm pretty close. The method finds the minimum amount of coins needed to make a certain amout of change by creating a table and using the results that are stored in the table to solve the larger problem without using recursion. public static int minCoins