TC (complexity)
Imagine you have a very big puzzle to solve. You have many pieces, and they all have to fit together perfectly to show you the whole picture. That's kind of like a computer program or an algorithm. An algorithm is a set of instructions that solves a problem or completes a task by putting together smaller steps to make one big process.
But just like a puzzle, some algorithms take longer to solve than others. tc (complexity) is how we measure how long it takes for an algorithm to solve a problem or complete a task. This measurement can help us compare different algorithms and find out which ones are the most efficient or fastest.
To measure tc (complexity), we count how many steps the algorithm has to take to complete the task. Each step is like a move you make when putting the puzzle pieces together. The faster an algorithm can complete the task, the lower its tc (complexity). If an algorithm takes a lot of steps to complete a task, it has a higher tc (complexity) and may not be the best choice for that problem.
So, in short, tc (complexity) is a way to measure how many steps an algorithm takes to complete a task, and the lower the tc (complexity), the faster and more efficient the algorithm is.
But just like a puzzle, some algorithms take longer to solve than others. tc (complexity) is how we measure how long it takes for an algorithm to solve a problem or complete a task. This measurement can help us compare different algorithms and find out which ones are the most efficient or fastest.
To measure tc (complexity), we count how many steps the algorithm has to take to complete the task. Each step is like a move you make when putting the puzzle pieces together. The faster an algorithm can complete the task, the lower its tc (complexity). If an algorithm takes a lot of steps to complete a task, it has a higher tc (complexity) and may not be the best choice for that problem.
So, in short, tc (complexity) is a way to measure how many steps an algorithm takes to complete a task, and the lower the tc (complexity), the faster and more efficient the algorithm is.