Dynamic programming is a technique used to solve complex problems by breaking them down into smaller, simpler modules. It involves comparing and combining solutions from previous analyses to determine the optimal solution. While similar to greedy algorithms, dynamic programming is better as it does not rely on local optima. Dynamic programming finds applications in fields like bioinformatics for problems involving sequence alignment, protein folding, and RNA structure prediction.

1. Dynamic programming

2. Introduction
 The concept of dynamic programming was framed in
order to solve the complex problems by diving them into
small and simpler modules
 It is known as optimal control
 In recent days, dynamic programming finds application
in solving the complex problems with the exponential
portion

3. Overview
 In dynamic programming technique, the solutions
obtained from previous analysis is compared, combined
and given into a single module that proves to be a best
solution for the problem
 When compared with the greedy algorithm technique,
dynamic programming is better as greedy algorithm is
based on local optimal results which might result in
error

4. Application of Dynamic programming
 There are various applications of dynamic
programming.
 The most significant application is in the field of
bioinformatics
 They are used in the sequence alignment and in protein
folding
 They are helpful in RNA structure prediction and in the
DNA-binding

5. Conclusion
 Dynamic programming method is hence discussed with
the methodologies and the different types of
implementations in both computer programming and in
mathematical optimization

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