**Title:** Dynamic Programming Simplified: A 10-Minute Crash Course

**SEO Keywords:** dynamic programming, algorithm optimization, problem-solving, computer science, coding, software development

**Intro:**
Dynamic programming is a powerful technique for solving complex problems by breaking them down into smaller subproblems. It's a crucial concept in computer science and algorithm optimization, but it can be intimidating to learn. In this 10-minute crash course, we'll demystify dynamic programming and show you how to apply it to your own coding projects.

**Main Blog Content:**
Dynamic programming is an iterative approach that solves problems by:

1. **Divide**: Break down the problem into smaller subproblems.
2. **Conquer**: Solve each subproblem recursively or with a simple algorithm.
3. **Combine**: Combine the solutions of the subproblems to solve the original problem.

Here's a step-by-step guide to implementing dynamic programming:

* Identify the optimal substructure: Find the smallest possible subproblems that can be solved independently.
* Compute the solution for smaller subproblems: Use recursion or iteration to compute the solutions for each subproblem.
* Store the results: Cache the solutions of the subproblems to avoid redundant computation.
* Combine the solutions: Use the stored solutions to solve the original problem.

Let's illustrate this with a simple example. Consider the Fibonacci sequence, where each number is the sum of the previous two (1, 1, 2, 3, 5, 8, ...). A naive approach would calculate each number recursively:
```java
int fibonacci(int n) {
    if (n <= 1) return n;
    return fibonacci(n-1) + fibonacci(n-2);
}
```
However, this has a time complexity of O(2^n), which is inefficient. Instead, we can use dynamic programming to store the solutions of smaller subproblems and combine them:
```java
int[] fibMemo = new int[100]; // memoization array

int fibonacci(int n) {
    if (n <= 1) return n;
    if (fibMemo[n] != 0) return fibMemo[n];
    int result = fibonacci(n-1) + fibonacci(n-2);
    fibMemo[n] = result; // store the solution
    return result;
}
```
By storing the solutions of smaller subproblems, we reduce the time complexity to O(n), making it much more efficient.

**TL;DR:** Dynamic programming is a problem-solving technique that breaks down complex problems into smaller subproblems. It involves dividing, conquering, and combining solutions to solve the original problem efficiently. By using memoization or caching, you can avoid redundant computation and reduce time complexity. Apply dynamic programming to your coding projects to optimize performance and improve problem-solving skills.

**Additional Resources:**

* Wikipedia: Dynamic Programming
* GeeksforGeeks: Dynamic Programming

**Takeaway:** In 10 minutes, we've learned the basics of dynamic programming and how to apply it to solve complex problems efficiently. Remember to identify optimal substructures, compute solutions recursively or iteratively, store results, and combine them to solve the original problem. Happy coding!