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What is Dynamic Programming: Examples, Characteristics, and Working

What is Dynamic Programming: Examples, Characteristics, and Working

Before diving into dynamic programming, it’s essential to have a firm grip on the programming fundamentals, data structures, and algorithms. Familiarity with concepts like recursion, memoization, and problem-solving strategies is beneficial. Additionally, a strong understanding of time and space complexity analysis will help evaluate the efficiency of dynamic programming solutions. Prepare yourself for this exciting journey by mastering these fundamental concepts.

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What is Dynamic Programming?

What is Dynamic Programming

Dynamic programming is a problem-solving technique that tackles complex problems by dividing them into smaller subproblems that overlap. It breaks down the problem into manageable parts and solves them individually to find an optimal solution.

  • It aims to find the optimal solution by efficiently solving these subproblems and combining their solutions. 
  • Dynamic programming stores the results of subproblems in a table or cache, allowing for efficient retrieval and reuse of previously computed solutions.
  • At its core, dynamic programming relies on two fundamental principles: optimal substructure and overlapping subproblems. 

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  • Optimal substructure implies that an optimal solution to a more significant problem can be constructed from optimal solutions to its smaller subproblems. 
  • The occurrence of identical subproblems during a computation is referred to as overlapping subproblems.
  • To apply dynamic programming, the problem must exhibit both of these properties. Once identified, the problem can be solved in a bottom-up or top-down manner. 
  • In the bottom-up approach, solutions to smaller subproblems are calculated first and then used to build up to the final solution. 
  • Conversely, the top-down approach begins with the original problem and recursively breaks it into smaller subproblems.
  • Dynamic programming in C++ can be used in various domains, including optimization problems, graph algorithms, and sequence alignment. 
  • It offers an efficient and systematic way to solve problems that may otherwise be computationally infeasible.
  • It reuses the previously solved subproblems, improving efficiency and accuracy in finding solutions.

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Dynamic Programming Examples

Dynamic Programming Examples

Dynamic programming is a very versatile approach to solving problems. Below are a few examples of how you can utilize dynamic programming algorithms. 

  • Fibonacci Sequence: One classic example is calculating the nth Fibonacci number using dynamic programming. By storing previously computed values, dynamic programming algorithm can avoid redundant calculations, resulting in significant performance improvements.
  • Shortest Path Algorithms: Dynamic programming is instrumental in solving shortest path problems, such as Dijkstra’s or Bellman-Ford’s algorithms. It finds the shortest path efficiently by incrementally building optimal paths from a source node to other nodes.
  • Longest Common Subsequence: Given two sequences, dynamic programming can efficiently find the longest common subsequence (LCS) between them. It avoids redundant computations by breaking the problem into smaller subproblems and storing intermediate results.
  • Matrix Chain Multiplication: Dynamic programming in Java is commonly used to optimize matrix chain multiplication. It can minimize the number of scalar multiplications required by finding the optimal parenthesization of matrix multiplications.
  • Knapsack Problem: The problem involves selecting a combination of items with maximum value while considering a weight constraint. Dynamic programming can be used to find the optimal solution by breaking the problem into smaller subproblems and utilizing the previously computed results.
  • Coin Change Problem: Given a set of coin denominations and a target value, dynamic programming can determine the minimum number of coins required to reach the target value. This problem is often solved using bottom-up dynamic programming, starting with smaller values and gradually building up to the target value.

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Working of Dynamic Programming

Working of Dynamic Programming

Dynamic programming avoids redundant calculations by storing the solutions to subproblems in a data structure, such as an array or a table. It allows for efficient retrieval of previously computed solutions when needed.

The general steps involved in implementing dynamic programming are as follows:

  • Identify the Problem: Determine the optimization problem that can be divided into overlapping subproblems. This problem should exhibit both optimal substructure and overlapping subproblem properties.
  • Define the State: Identify the variables or parameters that define the state of the problem. The state should concisely capture the essential information required to solve the problem.
  • Formulate the Recurrence Relation: Express the solution to a larger problem in terms of the solutions to its subproblems. This recurrence relation provides the mathematical relationship between the current state and its substrates.
  • Create a Memoization Table: Initialize a data structure, such as an array or a table, to store the solutions to subproblems. This table serves as a cache for storing and retrieving previously computed solutions.
  • Populate the Table: Iterate through the subproblems in a bottom-up manner, filling the table with solutions based on the recurrence relation. Start with the simplest subproblems and gradually build up to the larger ones.
  • Retrieve the Final Solution: Once the table is populated, the final solution can be obtained by accessing the value stored in the table corresponding to the original problem’s state.
Pseudo Code Example (Fibonacci sequence):
function fibonacci(n):
  table = new Array(n+1)
  table[0] = 0
  table[1] = 1
  for i from 2 to n:
    table[i] = table[i-1] + table[i-2]
  return table[n]

For example, this given illustration demonstrates the use of dynamic programming to compute the Fibonacci sequence. The process involves initializing a table with base cases (0 and 1) and subsequently populating it with solutions to subproblems (which is the sum of the previous two numbers). And finally, the solution is obtained by retrieving the value at the given nth index from the table.

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Advantages of Dynamic Programming

Advantages of Dynamic Programming

Leveraging the advantages of dynamic programming allows programmers to develop an efficient and effective solution to complex problems, making it a vital technique in algorithm design and optimization.

Here are some key advantages of dynamic programming:

  • Optimal Substructure: Dynamic programming is particularly effective when the problem exhibits optimal substructure. This property allows dynamic programming algorithms to break down the problem into smaller, overlapping subproblems, reducing redundancy and improving efficiency.
  • Memoization: Dynamic programming often involves memoization, which involves caching intermediate results to avoid redundant computations. By storing previously computed solutions, dynamic programming algorithms can quickly retrieve and reuse them, reducing their overall time complexity. Memoization helps eliminate repetitive calculations, leading to significant performance improvements.
  • Time Complexity Optimization: Dynamic programming algorithms can significantly reduce the time complexity of a problem by solving it in a bottom-up or top-down manner. By breaking the problem into smaller subproblems and solving them independently, dynamic programming eliminates redundant calculations and optimizes the overall time complexity of the solution.
  • Space Complexity Optimization: In addition to time complexity, dynamic programming can optimize space complexity. Some problems may require storing only a subset of intermediate results, minimizing the memory requirements. This space optimization ensures that dynamic programming solutions remain efficient, even for large-scale problems.
  • General Applicability: Dynamic programming in python is a versatile technique that is applied to various types of problems across different domains, such as computer science, operations research, and economics. Its flexibility and effectiveness make it a valuable tool for solving optimization, sequencing, scheduling, and resource allocation problems.

Disadvantages of Dynamic Programming

While dynamic programming is a powerful technique, it does have some disadvantages that developers should be aware of. They are as follows:

Increased Space Complexity: One major disadvantage of dynamic programming is the potential for increased space complexity. Dynamic programming frequently involves storing solutions to subproblems in a table or array, which can consume a significant amount of memory, especially for problems with large input sizes. It’s important to carefully analyze the space requirements and consider whether the trade-off in memory usage is worth the optimized time complexity.

Identifying and Formulating Subproblems: Another drawback is the complexity of identifying and formulating subproblems. Decomposing a problem into overlapping subproblems requires careful analysis and insight, and it can be challenging to determine the optimal subproblems and their relationships. This process often requires deep understanding and creative thinking, making dynamic programming less approachable for beginners or developers unfamiliar with the problem domain.

Dynamic Programming Vs. Greedy Algorithm

Below are the various differentiating factors between dynamic programming and the greedy algorithm. However, the factors may vary based on the specific type of problem.

Dynamic ProgrammingGreedy Algorithm
Solve problems by breaking them into subproblems.Makes locally optimal choices at each step.
Uses memoization or tabulation for subproblem caching.Does not consider the future consequences of choices.
Typically, this involves solving overlapping subproblems.It does not guarantee an optimal global solution.
An optimal solution is guaranteed.This may or may not lead to an optimal solution.
Time complexity can be improved by reusing solutions.Time complexity depends on the specific problem.
Requires careful analysis and insight into subproblems.Simpler to implement and less complex to analyze.
Suitable for problems with optimal substructure.Suitable for problems with the greedy choice property.
Examples include the Fibonacci sequence, knapsack problem.Examples include Huffman coding, minimum spanning trees.

Dynamic Programming Vs. Recursion

Recursion and dynamic programming sound the same but there are many differences between them, let’s analyze these one by one.

AspectDynamic ProgrammingRecursion
ApproachBottom-up (starting from base cases)Top-down (starting from the original problem)
Subproblem SolvingSolves each subproblem only onceCan solve the same subproblem multiple times
Overlapping SubproblemsExploits overlapping substructuresMight result in redundant computations
Time ComplexityGenerally more efficient due to memoization and avoiding duplicatesCan be less efficient due to redundant computations
Space ComplexityRequires additional storage for storing subproblem solutionsUsually requires less additional storage
Code ComplexityOften more complex due to setting up tables/arraysCan be simpler, but with potential performance trade-offs
Use CasesWell-suited for optimization problems, shortest path problems, and moreUseful for exploring all possibilities, like traversing trees or graphs
Example ProblemFibonacci number calculation, shortest path in a graphTower of Hanoi, recursive factorial

Remember that dynamic programming can often be implemented using a recursive approach as well, but it adds memoization (storing already computed values) to enhance efficiency and address the overlapping subproblem issue. The choice between dynamic programming and recursion depends on the problem’s nature and requirements.

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We’ve explored the various concepts of dynamic programming and associated terminologies in the blog, “What is dynamic programming?” We also broke down complex problems into smaller, overlapping subproblems, leading to optimal solutions. With this knowledge, you can tackle challenging algorithmic puzzles, optimize your code, and unleash your problem-solving prowess. Remember, practice makes perfect, so keep honing your skills and exploring real-world applications of dynamic programming.  


What is dynamic programming meaning?

In simple terms, dynamic programming in computer science and statistics is a method of solving problems by breaking them down into smaller subproblems.

Which languages use dynamic programming?

These languages are built to use dynamic programming: Python, Java, C++, C, JavaScript, Ruby, R, MATLAB

What is dynamic programming advantage?

  1. Efficiency: Reduces time complexity by avoiding redundant calculations.
  2. Optimization: Ideal for finding the best solution among feasible options.
  3. Memoization: Stores results of costly function calls to save time on repeated inputs.
  4. Simplicity: Often provides a more intuitive solution once the approach is understood.
  5. Generalization: Solutions can be adapted to solve a range of similar problems.
  6. Structured Approach: Offers a systematic method for tackling complex problems.
  7. Overlapping Subproblems: Solves each subproblem once, preventing repetitive computations.
  8. Bottom-up Computation: Starts with the smallest subproblems, ensuring all required data is available.
  9. Space Efficiency: Uses extra memory for significant time savings.
  10. Versatility: Applicable to a broad range of algorithmic and real-world problems.

What are dynamic programming limitations?

  1. Memory Usage: Can require significant memory to store solutions of all subproblems.
  2. Optimal Substructure: Not all problems have the property where the optimal solution can be constructed from optimal solutions of its subproblems.
  3. Initialization Overhead: Setting up tables or matrices can add extra initialization time.
  4. Complexity: Implementing dynamic programming solutions can be more complex than simpler recursive solutions.
  5. Not Always Optimal: For some problems, greedy algorithms can be more efficient.
  6. Overhead of Recursion: Recursive dynamic programming can lead to stack overflow for deep recursions

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Presenting Sahil Mattoo, a Senior Consultant Analytics & Data Science at Eli Lilly and Company is an accomplished professional with 14 years of experience across data science, analytics, and technical leadership domains, demonstrates a remarkable ability to drive business insights. Sahil holds a Post Graduate Program in Business Analytics and Business Intelligence from Great Lakes Institute of Management.