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Understanding data structures and algorithms allow you to curate optimized and efficient computer programs. A data structure can be defined as a location used to organize or store data. You must choose a data structure that conforms to your project. For example, if your task is to store data sequentially, then you can go for the array data structure. You should never confuse data structures with data types. A data structure is made up of a collection of data types. An algorithm on the other hand is a series of steps that are used to solve a particular problem. It accepts a set of inputs and returns the desired output.
The concept of dynamic programming involves optimizing over plain recursion. Meaning, we can use it to optimize a recursive solution with a repeated call for the same output. In dynamic programming, we store the solutions of sub-problems to save us the stress of re-computing them again later when they are needed. Doing this reduces to a great extent the complexities of time from exponential to polynomial. For example, suppose we have written a recursive solution for Fibonacci numbers, we are bound to face a time complexity. However, we can reduce this time complexity to linear if we optimize the solution by storing the results. In simple terms, we can define dynamic programming as an algorithmic technique that solves a problem by breaking it into smaller sub-problems. The solutions to these sub-problems will determine the optimal solution to the main problem.
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