• Anika Saini
• Jitendra Parmar

Dynamic Programming (DP) stands as a foundational optimization approach with vast programs across numerous fields. This evaluate gives a comprehensive exploration of DP, encompassing its ancient evolution, fundamental ideas, algorithmic strategies, and significant applications. From conventional issues just like the Fibonacci series to complex optimization challenges in economics, bioinformatics, and robotics, DP demonstrates its versatility. The paper examines optimization strategies, compares memorization and tabulation, and delves into kingdom-area reduction strategies. Applications in economics, bioinformatics, and robotics illustrate the real-world impact of DP. Advancing beyond conventional DP issues, the evaluation explores current traits. Approximate DP and its connection to reinforcement learning, parallel and distributed methods, and adaptive online variations imply the evolving landscape. The demanding situations of scalability, reminiscence efficiency, and multi-goal optimization are addressed, dropping mild on ability answers. The integration of DP with device learning opens new avenues for research and application.Dynamic Programming (DP) stands as a foundational optimization approach with vast programs across numerous fields. This evaluate gives a comprehensive exploration of DP, encompassing its ancient evolution, fundamental ideas, algorithmic strategies, and significant applications. From conventional issues just like the Fibonacci series to complex optimization challenges in economics, bioinformatics, and robotics, DP demonstrates its versatility. The paper examines optimization strategies, compares memorization and tabulation, and delves into kingdom-area reduction strategies. Applications in economics, bioinformatics, and robotics illustrate the real-world impact of DP. Advancing beyond conventional DP issues, the evaluation explores current traits. Approximate DP and its connection to reinforcement learning, parallel and distributed methods, and adaptive online variations imply the evolving landscape. The demanding situations of scalability, reminiscence efficiency, and multi-goal optimization are addressed, dropping mild on ability answers. The integration of DP with device learning opens new avenues for research and application.

robotics, scalability, memory efficiency, optimization, memorization, tabulation, bioinformatics

"Dynamic Programming: A comprehensive review of Algorithms, Applications and Advances", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.109-116, December-2018, Available :http://www.jetir.org/papers/JETIRGD06017.pdf

"Dynamic Programming: A comprehensive review of Algorithms, Applications and Advances", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp109-116, December-2018, Available at : http://www.jetir.org/papers/JETIRGD06017.pdf