How are recurrence relations used in mathematics and computer science?

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    Recurrence Relation
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SUMMARY

A recurrence relation defines each term of a sequence based on preceding terms, with notable examples including the Fibonacci sequence and binomial coefficients. The Fibonacci numbers are calculated using the formula F_n = F_{n-1} + F_{n-2}, while binomial coefficients are derived from Pascal's triangle. Linear differential equations can be interpreted as recurrence relations through the characteristic polynomial method, allowing for solutions based on distinct roots. This mathematical concept is integral to algorithm theory, automaton theory, and formal language theory in computer science.

PREREQUISITES
  • Understanding of Fibonacci sequence and its formula
  • Knowledge of binomial coefficients and Pascal's triangle
  • Familiarity with linear differential equations
  • Basic concepts of characteristic polynomials
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  • Study the relationship between recurrence relations and dynamic programming
  • Learn about the use of characteristic polynomials in solving linear differential equations
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Mathematicians, computer scientists, algorithm developers, and anyone interested in the theoretical foundations of sequences and their applications in programming and computational theory.

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Definition/Summary

A recurrence relation is an equation which defines each term of a sequence as a function of preceding terms.

The most well-known are those defining the Fibonacci numbers and the binomial coefficients.

An ordinary differential equation can be considered as a recurrence relation on the sequence of nth derivatives of a function (in which the 0th derivative is the function itself), and if linear can be solved using the characteristic polynomial method.

Equations

Fibonacci numbers:

Each Fibonacci number is the sum of the two preceding numbers, starting with 1 and 1:

F_n\ =\ F_{n-1}\ +\ F_{n-2}

1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89 ...

Binomial coefficients (Pascal's triangle):

Each binomial coefficient is the sum of the two coefficients "diagonally below", starting with 1:

\left(\begin{array}{c}n\\k\end{array}\right)\ =\ \left(\begin{array}{c}n - 1\\k\end{array}\right)\ +\ \left(\begin{array}{c}n-1\\k-1\end{array}\right)

1; 1 1; 1 2 1; 1 3 3 1; 1 4 6 4 1; 1 5 10 10 5 1; ...

Extended explanation

Characteristic polynomial:

This is not the same as the characteristic polynomial of a matrix or matroid

In a linear differential equation, the derivative may be replaced by an operator, D, giving a polynomial equation in D:

\sum_{n\,=\,0}^m\,a_n\,\frac{d^ny}{dx^n}\ =\ 0\ \mapsto\ \left(\sum_{n\,=\,0}^m\,a_n\,D^n\right)y\ =\ 0

Similarly, in a linear recurrence relation, the nth term may be replaced by an nth power:

\sum_{n\,=\,0}^m\,a_{k+n}\,P_{k+n}\ =\ 0\ \mapsto\ \sum_{n\,=\,0}^m\,a_n\,x^n\ =\ 0

If this polynomial has distinct (different) roots r_1,\dots,r_m:

\prod_{n\,=\,1}^m(D\,-\,r_n)\ =\ 0 or \prod_{n\,=\,1}^m(x\,-\,r_n)\ =\ 0

then the general solution is a linear combination of the solutions of each of the equations:

\left(D\,-\,r_n\right)y\ =\ 0 or P_{k+1}\ -\ r_nP_k\ =\ 0

which are the same as \frac{dy}{dx}\ =\ r_n\,y or \frac{P_{k+1}}{P_k}\ =\ r_n

and so is of the form:

y\ =\ \sum_{n\,=\,1}^m\,C_n\,e^{r_nx} or P_k\ =\ \sum_{n\,=\,1}^m\,C_n\,(r_n)^k

For a pair of complex roots (they always come in conjugate pairs) p\ \pm\ iq or r\,e^{\pm is}, a pair of C_ne^{r_nx} or C_n\,(r_n)^kmay be replaced by e^{px}(A\,cos(qx)\,+\,iB\,sin(qx)) or r^k(A\,cos(sk)\,+\,iB\,sin(sk))

However, if the polynomial has some repeated roots:

\prod_{p\,=\,1}^q\prod_{n\,=\,1}^p(D\,-\,r_n)^p\,y\ =\ 0 or \prod_{p\,=\,1}^q\prod_{n\,=\,1}^p(x\,-\,r_n)^p\ =\ 0

then the general solution is of the form:

y\ =\ \sum_{p\,=\,1}^q\sum_{n\,=\,1}^pC_{n,p}\,x^{p-1}\,e^{r_nx} or P_k\ =\ \sum_{p\,=\,1}^q\sum_{n\,=\,1}^pC_{n,p}\,n^{p-1}\,(r_n)^k

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Recurrences are subject to an entire branch of mathematics, of course one which directly connects to algorithm theory in computer science as well as automaton theory and the theory of formal languages.
 

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