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Yes, recursion can be visualised, often through the use of tree diagrams or fractal images.

Recursion, in computer science, is a method where the solution to a problem depends on solutions to smaller instances of the same problem. It can be a challenging concept to grasp, but visualisation can help to make it more understandable. One common way to visualise recursion is through the use of tree diagrams. Each node in the tree represents a recursive call, and the branches represent the different paths that the recursion can take. This can help to show how the recursion breaks the problem down into smaller parts, and how these parts are then combined to solve the original problem.

For example, consider the problem of calculating the factorial of a number (n!). This can be solved recursively by defining n! as n times the factorial of (n-1). A tree diagram for this problem would start with a node for n!, which would branch to nodes for (n-1)! and n. The (n-1)! node would then branch to nodes for (n-2)! and (n-1), and so on. This visualisation helps to show how the recursion reduces the problem to simpler problems, and how the solutions to these simpler problems are used to solve the original problem.

Another way to visualise recursion is through fractal images. Fractals are complex shapes that are self-similar, meaning they look the same at any scale. They are often created using recursive algorithms, which makes them a great way to visualise recursion. For example, the Sierpinski triangle is a fractal that can be created by recursively dividing a triangle into four smaller triangles and removing the middle one. This process can be repeated indefinitely, resulting in a complex pattern that is the same at any scale. This visualisation helps to show how recursion can be used to create complex patterns from simple rules.

In conclusion, visualising recursion can help to make this complex concept more understandable. Whether through tree diagrams or fractal images, these visualisations can help to show how recursion breaks problems down into simpler parts, and how these parts are combined to solve the original problem.

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