Dynamic Programming: Solving Complex Problems with Simplicity
Published: 2024-01-11 04:56:06

---
title: Dynamic Programming: Solving Complex Problems with Simplicity
published: true
date: 2023-11-21 09:53:30 UTC
tags: 
canonical_url: https://fanny.hashnode.dev/dynamic-programming-solving-complex-problems-with-simplicity
---

Dynamic programming is a powerful problem-solving technique that has been used in various fields, from computer science to economics.

It is a method for solving complex problems by breaking them down into smaller, simpler subproblems. In this article, we will explore the concept of dynamic programming, its applications, and how it can be used to solve complex problems with simplicity.

## **What is Dynamic Programming?**

Dynamic programming is a method for solving problems that involve making a sequence of decisions. It is based on the principle of optimality, which states that an optimal solution to a problem contains optimal solutions to its subproblems.

The idea behind dynamic programming is to break down a complex problem into smaller subproblems, solve each subproblem once, and store the solutions in a table. These solutions can then be used to solve larger subproblems until the entire problem is solved.

Dynamic programming is often used to solve problems that involve finding the shortest path, the longest common subsequence, or the maximum value of a function. It is also used in optimization problems, such as the knapsack problem and the traveling salesman problem.

### **The Steps of Dynamic Programming**

The following are the steps involved in solving a problem using dynamic programming:

1. Identify the subproblems: The first step in dynamic programming is to identify the subproblems that make up the larger problem. These subproblems should be smaller and simpler versions of the original problem.

2. Define the recurrence relation: Once the subproblems have been identified, the next step is to define a recurrence relation. This is a mathematical equation that expresses the solution to a subproblem in terms of the solutions to smaller subproblems.

3. Create a table: A table is used to store the solutions to the subproblems. The table should have enough space to store the solutions to all the subproblems.

4. Fill in the table: The next step is to fill in the table with the solutions to the subproblems. This is done by starting with the smallest subproblems and working up to the larger ones.

5. Use the table to solve the original problem: Once the table has been filled in, the solution to the original problem can be found by looking up the solution in the table.

## **Applications of Dynamic Programming**

Dynamic programming has a wide range of applications in various fields. Some of the most common applications include:

### **Computer Science**

In computer science, dynamic programming is used to solve problems such as shortest path algorithms, string matching, and sequence alignment. It is also used in data compression and image processing.

### **Economics**

In economics, dynamic programming is used to solve problems related to resource allocation, production planning, and optimal control. It is also used in finance to solve problems related to portfolio optimization and risk management.

### **Operations Research**

In operations research, dynamic programming is used to solve problems related to scheduling, inventory management, and network optimization. It is also used in transportation and logistics to optimize routes and minimize costs.

## **Approximate Dynamic Programming**

Approximate dynamic programming (ADP) is a variation of dynamic programming that is used to solve problems with large state spaces. It is often used in reinforcement learning, control theory, and optimization.

The main difference between ADP and traditional dynamic programming is that ADP uses function approximation to represent the value function instead of storing all the values in a table. This allows ADP to handle problems with large state spaces more efficiently.

### **The Advantages of ADP**

- It can handle problems with large state spaces more efficiently than traditional dynamic programming.

- It can be used to solve problems that are too complex to be solved using traditional dynamic programming.

- It can be used to solve problems that involve continuous state and action spaces.

### **The Disadvantages of ADP**

- It requires a good approximation function to accurately represent the value function.

- It can be computationally expensive to find the optimal approximation function.

- It may not always find the optimal solution to a problem.

## **Dynamic Programming and Optimal Control**

Dynamic programming is closely related to optimal control, which is a branch of control theory that deals with finding the optimal control policy for a system. Optimal control problems can be solved using dynamic programming, making it a powerful tool for solving complex control problems.

### **The Bellman Equation**

The [Bellman equation](https://medium.com/analytics-vidhya/bellman-equation-and-dynamic-programming-773ce67fc6a7) is a fundamental equation in dynamic programming and optimal control. It expresses the value of a state in terms of the value of the next state and the reward received for taking a particular action.

The Bellman equation is given by:

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1700560175780/d13e800e-ac5e-4992-9d28-e4cc2fe0d3f3.jpeg)

Where:

- V(s) is the value of state s

- R(s,a) is the reward received for taking action a in state s

- is the discount factor

- P(s'|s,a) is the probability of transitioning to state s' from state s when taking action a

The Bellman equation is used to find the optimal value function, which is the maximum value that can be achieved by following an optimal policy.

## **Dynamic Programming and Optimal Control: A Real-World Example**

![Robot arm](https://images.unsplash.com/photo-1518135714426-c18f5ffb6f4d?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wzMjkxMTJ8MHwxfHJhbmRvbXx8fHx8fHx8fDE3MDA1NTc3NDl8&ixlib=rb-4.0.3&q=80&w=1080)

by Chris Liverani ([https://unsplash.com/@chrisliverani](https://unsplash.com/@chrisliverani))

To better understand how dynamic programming and optimal control can be used in a real-world scenario, let's consider the example of a robot arm. The goal is to find the optimal control policy for the robot arm to reach a target position while avoiding obstacles.

The state of the robot arm can be represented by its position and velocity, and the action can be represented by the torque applied to the joints. The reward for reaching the target position is high, while the reward for colliding with an obstacle is low.

Using dynamic programming, we can break down this problem into smaller subproblems and find the optimal control policy for each subproblem. This allows the robot arm to reach the target position while avoiding obstacles, even in complex environments.

## **Conclusion**

Dynamic programming is a powerful problem-solving technique that can be used to solve complex problems with simplicity. It is based on the principle of optimality and involves breaking down a problem into smaller subproblems, solving each subproblem once, and storing the solutions in a table.

Dynamic programming has a wide range of applications in various fields, and its variation, approximate dynamic programming, is used to solve problems with large state spaces.

It is also closely related to optimal control, which deals with finding the optimal control policy for a system. By understanding the concept of dynamic programming and its applications, you can use it to solve complex problems and achieve optimal results.