Dynamic Programming#

This chapter explains dynamic programming, a method for solving complex problems with strategic optimization. By breaking down problems into smaller subproblems and using memorization and recursion, elegant and efficient solutions can be found. It’s like solving a puzzle by solving smaller pieces and putting them together to form the larger picture.

What this chapter covers:

  1. Introduction to Dynamic Programming: Establish a solid foundation by understanding the core principles of dynamic programming, its advantages, and the problems it best suits.

  2. Overlapping Subproblems and Optimal Substructure: Delve into the key concepts that underlie dynamic programming, namely identifying overlapping subproblems and exploiting optimal substructure.

  3. Fibonacci Series and Beyond: Begin with classic examples like solving the Fibonacci series and gradually progress to more intricate problems that involve complex optimization.

  4. Efficiency and Trade-offs: Understand the trade-offs involved in dynamic programming, including the balance between time and space complexity.

  5. Problem-Solving Strategies: Develop systematic strategies for approaching dynamic programming problems, from identifying subproblems to deriving recurrence relations.