2 Keys Keyboard | Detailed Explanations | Leetcode 650 | codestorywithMIK
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This is the 99th Video of our Playlist "Dynamic Programming : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a very good and famous problem : 2 Keys Keyboard | Multiple Approaches | Leetcode 650 | codestorywithMIK
I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.
Problem Name : 2 Keys Keyboard | Multiple Approaches | Leetcode 650 | codestorywithMIK
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My solutions on Github(C++ & JAVA) : https://github.com/MAZHARMIK/Intervie...
Leetcode Link : https://leetcode.com/problems/2-keys-...
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Summary :
Approach 1: Recursion + Memoization
Concept: This approach uses a recursive function to explore all possible ways to reach exactly n 'A's. It tries two main operations: copying the current string and pasting it, or just pasting the previously copied string.
Optimization: Memoization is used to store the results of subproblems to avoid redundant calculations.
Time Complexity: O(n^2) because there are n possible lengths, and for each length, up to n pastes might be considered.
Space Complexity: O(n^2) due to the 2D memoization table.
Approach 2: Bottom-Up + Mathematics
Concept: This dynamic programming approach builds up the solution from smaller subproblems. It relies on finding factors of n to minimize the number of steps. If i is divisible by a factor, we calculate the steps needed to reach that factor, then add the steps required to reach i by copying and pasting.
Optimization: By starting from the largest factors and working downward, we ensure the minimal steps are found early.
Time Complexity: O(n^2) due to checking each factor for all numbers up to n.
Space Complexity: O(n) as only a 1D array is used to store the minimum steps for each i.
Approach 3: Greedy
Concept: This approach greedily copies the current string when the remaining target is divisible by the current number of 'A's, and pastes until reaching n. It tries to maximize the number of 'A's added in each step.
Optimization: By always doubling the number of 'A's when possible, this approach minimizes the number of operations.
Time Complexity: O(n) because the loop iterates while counting up to n.
Space Complexity: O(1) as only a few variables are used to track progress.
✨ Timelines✨
00:00 - Introduction
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