Abstract:
Dynamic programming (DP) is a powerful algorithmic paradigm used to solve complex optimization problems by breaking them down into simpler subproblems. This approach is particularly effective for problems exhibiting overlapping subproblems and optimal substructure
properties. In this paper, we explore the principles of dynamic programming, demonstrating
its application through various classical problems such as the Knapsack problem, Fibonacci
sequence computation, and the Longest Common Subsequence problem. We present a systematic methodology for formulating DP solutions, including state definition, recurrence
relation establishment, and base case identification. Additionally, we discuss both memoization and tabulation techniques, providing insights into their respective advantages and tradeoffs. Through extensive analysis, we evaluate the time and space complexity of dynamic programming algorithms, highlighting their efficiency compared to naive recursive approaches.
Finally, we address common pitfalls in DP problem-solving and propose best practices for
algorithm design and implementation. Our findings underscore the significance of dynamic
programming as an essential tool in computer science and operations research, with applications spanning fields such as bioinformatics, finance, and artificial intelligence. This abstract
provides a concise overview of the dynamic programming approach, its methodology, applications, and significance in solving optimization problems.
