time and space complexity calculator

Calculate the time and space complexity of your code using Big O notation.

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Calculate the time and space complexity of your code with this powerful app. Get insights into the efficiency of your algorithms and optimize them for better performance. Try it now!

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calculation time complexity and space complexity

How do they plot the graph shown in Figure #2? They just plotted the graph for the particular complexity expression. For instance, for O( n2 ) they plot y = x**2, for O(n log(n)) they plot y = n * math.log(n). More on reddit.com

r/learnpython

July 15, 2023

java - How do I calculate time complexity and space complexity of this solution? Could you please explain it to me? - Stack Overflow

It is leetcode problem 94. Binary Tree inorder traversal. /** * Definition for a binary tree node. * public class TreeNode { * int val; * TreeNode left; * TreeNode right; *... More on stackoverflow.com

stackoverflow.com

Can somebody here explain how to calculate time & space complexity for backtracking problems?

is the time complexity O(2^n) since I am making 2 decisions and the height of the decision tree is n? You are making O(2N) "decisions", but each of those requires an average N/2 work. See my other post in these comments for why this is. Thus the runtime is O(N * 2N). Specifically, that work happens in this expression, which is somewhat hidden by python magic: subset + [nums[i]] This expression makes a copy of subset, which requires time proportional to the length of subset, which is on average N/2. is the space complexity O(n) since at max I will only pass in n elements to the get_subsets function? The powerset itself requires O(N * 2N) space, but I believe you are interested in the additional space used beyond that. Recursive calls use space on the stack. Each recursive call you make uses O(1) space (ignoring memory allocated that goes directly into the result). So the total amount of space used by your algorithm will be proportional to the depth of your recursion tree, as that will be that maximum number of stack frames you have live at any given time. Notice that in your algorithm your recursion depth is never deeper than N. Thus your space usage is O(N). (Edit: Some pedants may point out that not ALL recursive calls are required to use space on the stack, due to something called tail recursion. However, your algorithm is not tail recursive, and python does not optimize tail recursion, so none of that applies here.) More on reddit.com

r/learnprogramming

May 24, 2021

where can I learn about how to calculate space complexity?

Space complexity is just a measure of memory space taken by algorithm (usually including space taken by input parameters, but sometimes this term is used when talking only about the additional space taken by the algorithm). First you need to decide the metric you will measure. For example it could be number of bytes. Alternatively it could be number of primitive values (number of integers, floats, maybe even strings - it's up to you to define the metric). Once you have the metric you literally just look at the program and count how much space it takes in comparison to n which presumably is input size. So for example if our metric is number of bytes and our algorithm uses 5 2-byte integers and 2 n-sized arrays of 4-byte integers the space complexity is: 2*4*n + 5*2 = 8n + 10. So space complexity is 8n+10 bytes. Asymptotic notation (big O) is actually totally separate concept. It is indeed basically always tied together when talking about time/space complexity, but it really has nothing to do with that. It's just used, because often when comparing algorithms we are only interested in their asymptotic behavior + the constants will usually vary widely from implementation to implementation so it's unlikely to be useful when talking about algorithms in general. More on reddit.com

r/learnprogramming

February 15, 2023

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