Multiple recursion

As explained in Modern C

Multiple recursion is recursion in which one invocation makes more than one recursive call.

Multiple recursion occurs when one invocation makes more than one recursive call. The Fibonacci function demonstrates the pattern by calling itself with n−1 and n−2. section 7.3

Fibonacci and repeated work

For fib(4), the recursive call tree includes fib(2) twice. Recomputing those identical subproblems makes execution time exponential in n, often ruling out the direct recursive algorithm in practice. A cache stores computed values and changes the running time to linear in n, trading storage for computation. section 7.3

Figure 7.2. Recursive call fib(4)

Use the recursive call tree for fib(4) to trace where multiple recursion duplicates computation. section 7.3

How Modern C covers it

How deep it goes

Explains call duplication and exponential versus linear time, but defers implementation details and concrete timing measurements.

Examples

Uses fib(n), the fib(4) call tree, cached Fibonacci code, and timing exercises.

What it assumes

Assumes familiarity with recursive functions and introduces repeated computation through a concrete Fibonacci implementation.

What you can do after

Evaluate recursive performance and apply caching to eliminate repeated subproblem computation.

How it connects

Connects recursion with caching, algorithms, storage–computation tradeoffs, and computational complexity.

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Also explained in

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  Modern C, Third EditionMultiple recursion is recursion in which one invocation makes more than one recursive call.

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Related concepts

• exponential time complexityproduces · Exponential time complexity is a computational growth rate in which an algorithm’s running time increases exponentially with input size.

This explainer was generated by Manning's knowledge graph from the text of Modern C. The section links point to the parts of the book it draws on.