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Recursion solves a problem by invoking the same procedure on smaller instances, with base cases answered directly. For merge sort, an empty or one-entry array is already sorted, and every larger array is split into two shorter parts. The decreasing lengths justify termination; a self-call without a progress rule would not suffice.

A recursive procedure refers to itself, directly or through another procedure. A recursive call starts another invocation, with its own arguments and local variables; the caller can wait for its returned output. A base case supplies an answer without further recursive calls. To establish algorithm termination, also show that every recursive call progresses in a well-founded relation. “Smaller” means smaller in that chosen relation: argument values need not all decrease, and the arguments need not even be numbers.

Recursion is especially expressive when a data structure already decomposes into smaller structures. A binary tree has two child subtrees, so structural recursion can follow its shape. A short rule for each case can describe an entire traversal or computation. The distinction between a recursive procedure and the recursive process it generates helps connect that compact description to its resource use.^[ref]

An invocation made by a recursive procedure of itself, directly or through another procedure. Each invocation has its own arguments and local variables; a returning invocation supplies an output to its caller.

A computation that retains pending operations while further calls run, then completes those operations as results return. A recursive procedure can instead describe an iterative process: passing an updated accumulator onward may leave no arithmetic pending. Avoiding growing call stack storage in that case depends on the implementation. A fixed number of variables also does not imply fixed storage in bits when their integer values grow.^[ref]

Strong induction establishes a proposition $P(n)$ for all integers $n\ge n_0$. Prove $P(n_0)$; then, for each $n\ge n_0$, assume every $P(k)$ with $n_0\le k\le n$ and prove $P(n+1)$. The induction hypothesis includes all earlier cases, so the inductive step can use whichever smaller indices it needs. It cannot use $P(n+1)$ itself.^[ref]

This is useful when a construction breaks into smaller pieces of varying sizes. A prime factorization splits a composite number into smaller integer divisors, which need not be one less than the original integer. A recursive procedure can likewise call itself on several smaller inputs. The available cases should match those dependencies. Strong induction lets the mathematical proof follow the construction rather than forcing every step through the immediately preceding size.

Every integer $n\ge 2$ has a prime factorization. The induction base case $2$ is prime. For a larger integer, either it is prime or it is $ab$, with $2\le a,b<n$. The induction hypothesis supplies prime factorizations of both smaller integer divisors; multiplying them gives one for $n$. This proves existence, while uniqueness requires the additional argument under the fundamental theorem of arithmetic.^[ref]

For the chosen number $84$, one possible recursive decomposition isThe step at $84$ needs results for $6$ and $14$, rather than a result for $83$. Each composite number in this factorization tree splits into strictly smaller positive integers, so recursive descent terminates at prime numbers. The tree describes one construction; another choice of integer divisors can give a differently shaped factorization tree.

The same pattern explains the usefulness of strong induction for algorithm correctness. When a recursive procedure returns answers for smaller inputs, the induction hypothesis can justify those answers; the remaining task is to justify how the current call combines them. Strictly decreasing a nonnegative integer size measure establishes termination. Correctness also requires each call's conditions and its combination rule; termination alone does not make an algorithm correct.