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By : user2955906
Date : November 22 2020, 10:48 AM
it fixes the issue I thinky the code you provided is kind of chaotic. So this post is more about the conceptual algorithm instead of the real algorithm. This can differ a bit since for instance insertion in an ArrayList is not O(1), but I'm confident that you can use good datastructures (for instance LinkedLists) for this to let all operations run in constant time.
What your algorithm basically does is the following: code :
``````n+n^2+n^4+n^6+...n^(log r)
^  ^                    ^
|  \-- first iteration  \-- end of algorithm
\-- insertion
`````` ## Recursive Algorithm Time Complexity: Coin Change

By : Jorge Naranhoe
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , The two pieces of code are the same except that the second uses recursion instead of a for loop to iterate over the coins. That makes their runtime complexity the same (although the second piece of code probably has worse memory complexity because of the extra recursive calls, but that may get lost in the wash).
For example, here's partial evaluation of the second count in the case where S = [1, 5, 10] and m=3. On each line, I expand the left-most definition of count.
code :
``````  count(S, 3, 100)
= count(S, 2, 100) + count(S, 3, 90)
= count(S, 1, 100) + count(S, 2, 95) + count(S, 3, 90)
= count(S, 0, 100) + count(S, 1, 99) + count(S, 2, 95) + count(S, 3, 90)
= 0 + count(S, 1, 99) + count(S, 2, 95) + count(S, 3, 90)
`````` ## DP Coin Change Algorithm - Retrieve coin combinations from table

By : Ali-T
Date : March 29 2020, 07:55 AM
will help you Sure you can. We define a new function get_solution(i,j) which means all solution for your table[i][j]. You can think it returns an array of array, for example, the output of get_solution(4,3) is [[1,1,1,1],[2,1],[2,2],[3,1]]. Then: ## What is the time complexity of this coin changing combination algorithm?

By : Arun Singh
Date : March 29 2020, 07:55 AM
wish helps you Assume a case where amount, n, is very large and the values of each coin is very small compared to n and let the size of the coin array be c. In fact, in the worst case, we can assume the value of every coin to be about 1. In the tree representing the call stack that your solution builds, each node would branch c times. Each level of the tree subtracts the value of a coin (in the worst case is about 1) from n so the depth (or height) of the tree would be n. So we're looking at a c-branch tree with height n. The number of vertices, V = c^0 + c^1 + c^2 + c^3 + ... + c^(n-1) + c^n. You can see what this series reduces to here. The calculation for number of edges, E, is similar. This algorithm has O(c^n) time complexity. ## What is the time complexity of this coin change algorithm?

By : cybermole
Date : March 29 2020, 07:55 AM ## Why does the greedy coin change algorithm not work for some coin sets? 