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Binomial Coefficients Revisited
The binomial coefficient \binom{n}{k} was originally defined in terms of the factorial notation, and with our recursive definitions of the factorial notation, we also have a complete and legally-correct definition of…
Binomial Coefficients Revisited
The binomial coefficient \(\binom{n}{k}\) was originally defined in terms of the factorial notation, and with our recursive definitions of the factorial notation, we also have a complete and legally-correct definition of binomial coefficients. The following recursive formula provides an efficient computational scheme.
Let \(n\) and \(k\) be integers with \(0\le k\le n\). If \(k=0\) or \(k=n\), set \(\binom{n}{k}=1\). If \(0\lt k\lt n\), set \[\begin{aligned}\end{aligned}\] This recursion has a natural combinatorial interpretation. Both sides count the number of \(k\)-element subsets of \(\{1,2,\dots,n\}\), with the right-hand side first grouping them into those which contain the element\(n\) and then those which don't. The traditional form of displaying this recursion is shown in . This pattern is called Pascal's triangle. Other than the \(1\)s at the ends of each row, an entry of the triangle is determined by adding the entry to the left and the entry to the right in the row above.
Xing was intrigued by the fact that he now had two fundamentally different ways to calculate binomial coefficients. One way is to write \(\binom{n}{m}=P(n,m)/m!\) and just carry out the specified arithmetic. The second way is to use the recursion of Pascal's triangle, so that you are just performing additions. So he experimented by writing a computer program to calculate binomial coefficients, using a library that treats big integers as strings. Which of the two ways do you think proved to be faster when \(n\) say was between \(1800\) and \(2000\) and \(m\) was around \(800\)?
Practice (1)
Try each one on paper first. Reveal the answer to check; verified ones can be opened in the solver for every step.
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Write a couple of sentences to explain why \(C(n-1,k-1)\) counts the number of \(k\)-element subsets of \(\{1,2,\dots,n\}\) that contain the element \(n\).
Symbols used here
Number of k-element subsets of n things: n!/(k!(n−k)!).
Chance of A; chance of A given that B happened.
Inequalities that allow equality; < and > exclude it.
n × (n−1) × … × 1; the number of orderings of n things. 0! = 1.
Add a_k for k = 1 up to n.
Multiply a_k for k = 1 up to n.
The set with no elements; the number of elements of A.
Questions people ask
Permutation or combination?
Ask whether order matters. A lock code is a permutation (order matters); a hand of cards is a combination (it does not).
What is a graph in this sense?
Dots (vertices) joined by lines (edges) — not a plot. Road maps, social networks and molecules are graphs; questions like "is there a route" and "how few colours" are graph theory.
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Parts of this page are adapted from Keller & Trotter, Applied Combinatorics (CC BY-SA 4.0). Condensed and re-explained here; errors are ours.
Plus en Combinatorics & Graph Theory
The counting principlesPigeonhole principle and inclusion–exclusionBinomial coefficients and Pascal's triangleRecurrences and generating functionsGraphs: vertices, edges, degreesPaths, cycles, trees, Euler and HamiltonColouring and planar graphs