Python Coding: Learn To Code Fast. Python For Data Analysis And Machine Learning. Advanced Methods To Learn How To Create Codes. Practical Programming Strategies For Beginners. by Wall Eric

Author:Wall, Eric [Wall, Eric]
Language: eng
Format: epub, azw3, pdf
Published: 2020-09-27T16:00:00+00:00

Greed

Dynamic programming

Let’s take these one at a time. Divide-and-conquer is self-explanatory – the problem is broken down into small subproblems and the results of each one combined into an overall solution. This has to be one of the most common techniques to solve problems and is perhaps the most common approach to the design of algorithms.

Greedy algorithms involve optimization and combination. In short, it means to take the shortest path to the most useful solution for local problems, all the while hoping that somehow it will all lead to the global solution.

Dynamic programming is most useful when the subproblems start to overlap. This is not the same as the divide-and-conquer paradigm. Instead of breaking the problem down into individual subproblems, intermediate solutions get cached and then used in a later operation. It does use recursion like divide-and-conquer but, with dynamic programming, we compare the results at different times. This provides a boost in terms of performance for some types of problems; it can be quicker to retrieve a previous result than it is to go through recalculating it.

Backtracking and Recursion

Recursion is incredibly useful in terms of divide-and-conquer but it can be hard to see exactly what is going on; each of the recursive calls spins off into another recursive call. There are two types of cases at the heart of a recursive function:

Base case – this tells recursion when it should terminate

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