Skip to content
Advertisement

Tag: dynamic-programming

Why is this (presumably more efficient) dynamic algorithm being outperformed by the naive recursive version?

I have the following problem as homework: Write a O(N^2) algorithm to determine whether the string can be broken into a list of words. You can start by writing an exponential algorithm and then using dynamic programming to improve the runtime complexity. The naive exponential algorithm which I started out with is this: I then adapted this into the following

Nth Fibonacci in python

I’m a Java programmer learning python. This algorithm computes the nth fibonacci number using recursion + memoization. I don’t understand why I’m seeing this error “IndexError: list index out of range” when running the program in python. Can anybody help? Thanks a ton! Answer I made few changes in your code to get nth Fibonacci no.(there might be other way

Getting KeyError in the following code to find “minimum number square to the number”

I am getting the error in the following code for the above stated problem using memoization please help me find the error and correct the code. Answer It would help if you provided the full error output, which should include the line number and surrounding code. However, I suspect the issue lies in the following line: You’re checking whether the

Dynamic Programming approach issue

Alice goes for jogging every day for N meters. Sometimes she runs and sometimes she walks. Her walking speed is 1m/s and running speed is 2m/s . Given the distance up to which she does jogging, calculate the number of ways she can do jogging. example: Input: 3 (total distance covered during jogging) Output: 3 (possible case) Explanation: Alice could

Advertisement