The Greedy Algorithm is a problem-solving technique that follows the heuristic of making locally optimal choices at each step with the hope of finding a global optimum solution. It is commonly used for optimization problems where you need to find the best solution among a set of choices.

Here's an example of how you can use the Greedy Algorithm pattern to solve a coding problem:

Problem: Given a set of coins of different denominations and a target amount, find the minimum number of coins needed to make up that amount. Assume you have an infinite supply of each coin denomination.

Example:
Input: coins = [1, 5, 10, 25],
amount = 36
Output: 3
Explanation: The minimum number of coins needed to make 36 cents is 3 (1 coin of 25 cents, 1 coin of 10 cents, and 1 coin of 1 cent).

Solution: To solve this problem using the Greedy Algorithm, we can follow these steps:

1. Sort the coins array in descending order to start with the highest denomination first.
2. Initialize a variable called "count" to keep track of the number of coins used.
3. Iterate over the coins array:
   • While the current coin denomination is less than or equal to the remaining amount, subtract the coin value from the amount and increment the count.
   • Repeat this step until the remaining amount becomes 0 or no more coins can be used.
4. Finally, return the count, which represents the minimum number of coins needed.

Here's the implementation in Python:

def coinChange(coins, amount):
    coins.sort(reverse=True)
    count = 0
    for coin in coins:
        while amount >= coin:
            amount -= coin
            count += 1
    if amount > 0:
        return -1  # If it's not possible to make up the amount with given coins
    return count

# Test the function
coins = [1, 5, 10, 25]
amount = 36
print(coinChange(coins, amount))  # Output: 3

In this example, the Greedy Algorithm works because at each step, we select the largest coin denomination that is less than or equal to the remaining amount. By doing so, we minimize the number of coins used to reach the target amount.

Note that the Greedy Algorithm doesn't always guarantee an optimal solution for every problem. It's important to analyze the problem's constraints and ensure that the greedy approach will lead to the correct answer