Setting up & solving counting recurrences
Many counting problems are easiest to attack recursively: express the count for size n in terms of smaller sizes by conditioning on a first choice. The hard part is the setup — you must argue that the cases are exhaustive and disjoint — after which characteristic roots or iteration produce the closed form.
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Method: how to approach it
The order below is what examiners expect to see, and each step carries its own marks.
- Condition on a first decisionWhat happens at the first position, or whether a particular element is included. This splits the count into cases.
- Check the cases are disjoint and exhaustiveEvery object must fall into exactly one case, or the recurrence overcounts or misses.
- Write the recurrence with base casesInclude enough bases to cover every value the recurrence cannot reach.
- SolveUse characteristic roots for linear recurrences, or iterate and telescope for first-order ones.
Worked example
How many binary strings of length n contain no two consecutive 1s?
- Condition on the first character. If it is 0, the rest is any valid string of length n−1: aₙ₋₁ ways.
- If it is 1, the next must be 0, and the rest is any valid string of length n−2: aₙ₋₂ ways.
- These cases are disjoint and cover everything, so aₙ = aₙ₋₁ + aₙ₋₂.
- Bases: a₁ = 2 (strings 0, 1) and a₂ = 3 (00, 01, 10).
Answer. aₙ = aₙ₋₁ + aₙ₋₂ with a₁ = 2, a₂ = 3 — the Fibonacci numbers shifted, giving aₙ = Fₙ₊₂.
Where marks get dropped
These are the specific errors that cost credit on setting up & solving counting recurrences questions — QED's rubric penalises each of them separately.
- Setting up cases that overlap, which counts some objects twice.
- Giving one base case for a two-step recurrence, leaving it undetermined.
- Not justifying that the cases are exhaustive. The recurrence is only as good as that argument.
Practise this until it is automatic
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Setting up & solving counting recurrences — frequently asked questions
How do I know what to condition on?
Look for a decision that reduces the problem to the same problem on a smaller input — the first symbol, the largest element, or whether a specific item is used.
Why do Fibonacci numbers appear so often?
Because "one step or two steps" decompositions are ubiquitous: tilings, no-two-consecutive constraints, and staircase climbing all reduce to aₙ = aₙ₋₁ + aₙ₋₂.
When should I use generating functions instead?
When the recurrence is inhomogeneous or the structure is a convolution — generating functions turn convolution into multiplication and handle Catalan-style recurrences cleanly.
The rest of Combinatorics
Counting principles, permutations, combinations. Each subtopic below has its own method, worked example and mark-losing traps.
- 1Sum & product rules
- 2Permutations & combinations
- 3Binomial theorem & Pascal’s triangle
- 4The pigeonhole principle
- 5Inclusion–exclusion
- 6Counting with repetition (stars & bars)
- 7Derangements & counting surjections
- 8Double counting & bijective proofs
- 9Setting up & solving counting recurrences
- 10Generating functions — an introduction
- 11Multinomial coefficients & repeated items
- 12Hockey stick & Vandermonde identities
- 13Catalan numbers & lattice-path counting
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