As questions regarding sequences that verifies a linear recurrence relation with constant coefficients are posted very often on this site and that there appear to be no reference post about it, so I decided to write one.
I was also dissatisfied with pointing to the wikipedia or the brillant links to cite only the commonly referenced by people answering these questions.
I. Definition
- Here are some other examples
- And some that are not
- More generally they can be written
II. One term dependency
- A special case, the telescoping sum
- The geometric sequence
- Introducing a RHS
III. The homogeneous equation
- A general solution is a sum of a two solutions
- The characteristic equation
- Finding a closed form for the sequence
IV. Finding particular solutions
- RHS is of the form $P(n)a^n$
- Examples
- A little refinement
- RHS is a sum of such terms
- RHS is $f(n)\,a^n$ where $f$ is not a polynomial
V. Some examples and applications
- The Fibonacci sequence
- Lucas numbers
- Pisot numbers whose powers are almost integers
- Calculating $x^n+y^n$
- Example
- An example with a bit of everything
VI. Theoretical justification of $\operatorname{Span}({r_i}^n)$
- The $2$-dimensional problem
- The $3$-dimensional problem
- Generalisation, Newton's identities
- Solving with linear algebra
Feel free to add additional answers, for the points I wasn't able to cover, like techniques for some particular cases, or more advanced content for the theoretical part.
Best Answer
I. Definition:
A sequence verifying a linear induction relation with constant coefficients, is a sequence for which the current term is a linear combination of its predecessors.
Perhaps the most known example is the Fibonacci sequence:
$$F_n=F_{n-1}+F_{n-2}$$
Here are some other examples:
And some that are not:
More generally they can be written:
Where the relation $\Big(\sum\limits_{k=0}^p a_k\,x_{n+k}=0\Big)$ is called the associated homogeneous equation, and $f(n)$ the RHS.
Of course, there is no real difference between for instance $x_n=ax_{n-1}+bx_{n-2}$ and $x_{n+2}=ax_{n+1}+bx_{n}$ or even $x_{n+1}-ax_n-bx_{n-1}=0$, it's just a matter of presentation and starting index.
We will see that the "normalized" form allows to define a characteristic equation that will help us in solving the equation. For now just remember that it is a linear combination of some previous and subsequent terms of a sequence.
II. One term dependency:
Before entering the subject of the general method to solve such equations, let start with the initial $1$-dimensional case, where the current term depends only on a single predecessor.
A special case, the telescoping sum:
Among these, the easiest of all is the one below,
$$x_{n+1}=x_n+f(n)\iff x_{n+1}-x_n=f(n)$$
Notice that when we perform a summation of all terms, then only the beginning and the ending terms remain, while the others are cancelling out.
$\require{cancel}\begin{array}{lll} x_{n+1}&-&\cancel{x_n}&=&f(n)\\ +\cancel{x_{n}}&-&\cancel{x_{n-1}}&=&f(n-1)\\ +\cdots&&\cdots&&\cdots\\ +\cancel{x_{2}}&-&\cancel{x_{1}}&=&f(1)\\ +\cancel{x_{1}}&-&x_0&=&f(0)\\ \hline x_{n+1}&-&x_0&=&\sum\limits_{i=0}^n f(i)\end{array}$
Which gives you an expression for the general term:
Notice that when $f$ is a constant function, i.e. $x_{n+1}=x_n+a$ then we get the usual arithmetic sequence $x_n=x_0+\sum\limits_{i=0}^{n-1} a=x_0+na$.
The geometric sequence:
Then comes sequences that only involve two terms, like $ax_n+bx_{n-1}=0$, in which $a,b\neq 0$. We can always put it in the reduced form
$$x_n=r\, x_{n-1}$$
This is the well known geometric sequence of reason $r$ and it can easily be shown by induction that
Note that we can also have a bigger gap than just $1$ between indexes, as in $x_{n+2}=r\, x_n$ which solve almost in the same way, but with separated branches for even and odd indexes $\begin{cases}x_{2n}=r^n\, x_0\\x_{2n+1}=r^n\, x_1\end{cases}$
And we can generalize to any sequence verifying $x_{n+p}=r\, x_n$, we just have $p$ branches, according to the initial values $x_0,\cdots,x_{p-1}$
Introducing a RHS:
Assume we now have $$x_n-r\,x_{n-1}=f(n)$$
Notice that if we set $x_n=r^n\, u_n$ then we get:
$r^nu_n-r\cdot r^{n-1}u_{n-1}=f(n)\iff u_n-u_{n-1}=\dfrac{f(n)}{r^n}=g(n)\ $ and we have reduced the problem to the telescopic case seen above.
III. The homogeneous equation:
A general solution is a sum of a two solutions:
As for linear ODEs with constant coefficients, the linear induction relations share the same notion of associated homogeneous equation.
Assume that we have two solutions $x_n$ and $y_n$ of our equation (I show it with a $3$-dimensional equation, but this is just for clarity of presentation. It extends without any complications to an equation with more dependent terms):
$\begin{cases}a\,x_n+b\,x_{n-1}+c\,x_{n-2}=f(n)\\a\,y_n+b\,y_{n-1}+c\,y_{n-2}=f(n)\end{cases}$
we obtain after subtraction of these two, and setting $h_n=x_n-y_n$ that $h_n$ is a solution of the homogeneous equation:
$$a\,h_n+b\,h_{n-1}+c\,h_{n-2}=0$$
Therefore in order to solve the general equation with RHS, it suffice to find a general solution $h_n$ of the homogeneous equation, and one particular solution $\pi_n$ of the full equation to get all the solutions, and they will have the form:
The characteristic equation:
We call the characteristic equation associated with the (homogeneous) linear induction relation with constant coefficients $\sum\limits_{k=0}^p a_k\,x_{n+k}=0$ the following polynomial:
Here are some examples to understand how it works when you need to shift the indexes:
$\begin{array}{lll} x_{n+1}=ax_n &\iff x_{n+1}-ax_n=0 &\iff p(X)=X-a\\ x_{n}=ax_{n-2} &\iff x_{n+2}-ax_n=0 &\iff p(X)=X^2-a\\ x_{n}=ax_{n-1}+bx_{n-2} &\iff x_{n+2}-ax_{n+1}-bx_n=0 &\iff p(X)=X^2-aX-b\\ ax_{n+1}+bx_{n}+cx_{n-3}=0 &\iff ax_{n+4}+bx_{n+3}+cx_n=0 &\iff p(X)=aX^4+bX^3+c\\ \end{array}$
In the following course of this post, we will call the roots $r_i$ of this characteristic polynomial and their multiplicity $m_i$ (i.e. $m=1$ for a single root, $m=2$ for a double root, and so on).
As a special case, we will also settle for the convention $m=0$ if some particular value $r$ is not a root of the characteristic polynomial (i.e $p(r)\neq 0$).
Finding a closed form for the sequence:
In this paragraph I'll show the general method to solve the homogeneous equation without any justifications, for the theoretical aspects please refer to paragraph (VI).
Let assume the characteristic polynomial has roots $r_i$ with multiplicity $m_i$ for $i=1..k$ then the solution is a sum of the following terms for each root $r_i$.
In case the roots are complex, but initial terms and coefficients of the equation are all real, then the roots will in fact appear by pairs of conjugated complexes.
We can regroup $r=|r|e^{it}$ and $\bar r=|r|e^{-it}$ and use De Moivre formula to transform $a\,r^n+b\,\bar r^n$ into
This is for single roots, but it extends similarly to roots of higher multiplicity, just replace $\alpha,\beta$ by polynomials of higher degree.
IV. Finding particular solutions:
RHS is of the form $P(n)a^n$:
Generally in the exercises, you will be asked to solve the full equation with a RHS which is of the form $P(n)$ or $P(n)\,a^n$ with $P$ some polynomial.
Notice that $\,P(n)$ can also be written $\,P(n)\,1^n\, $ with $a=1$, so we don't need to solve this case separately, it follows exactly the same process.
If $a$ is not a root of the characteristic polynomial, then we can search for a particular solution of the form $Q(n)\,a^n$ where $Q$ is a general polynomial of the same degree than $P$.
If $a$ is a single root of the characteristic polynomial, then you have to raise the degree of $Q$, meaning we can search for solution of the form $Q(n)\,a^n$ where $\deg(Q)=\deg(P)+1$
Examples:
Assume $2$ is not a root and $RHS=3\times2^n$, you can search for particular solutions $Q(n)=\alpha\,3^n$
Assume $1$ is not a root and $RHS=3n^2+5$, you search for particular solutions $Q(n)=\alpha\,n^2+\beta\,n+c$
Assume $-1$ is a root, and $RHS=(-1)^n$, you can search for particular solutions $Q(n)=(\alpha\,n+\beta)(-1)^n$
A little refinement:
In fact we can ignore the terms of lower degree of $Q(n)$ since they will cancel out because they are already part of the homogenous equation solution.
e.g. if $r$ is a double root, then the homogeneous equation has a general solution $(an+b)r^n$.
Assume the $RHS=(5n)\,r^n$ then we search for $\deg(Q)=\deg(5n)+2=3$ therefore we should search for a particular solution
$$\require{cancel}Q(n)\,r^n=\big(\alpha\,n^3+\beta\,n^2+\underbrace{\cancel{\gamma\,n+\delta}}_\text{already solution of homogenous eq.}\big)\,r^n$$
This little refinement often speeds up a bit the solving of the exercise.
RHS is a sum of such terms:
In this case, we proceed following the exact same method for each term, and the particular solution has to be searched as a sum of all terms.
I presented an example in (V.)
RHS is $f(n)\,a^n$ where $f$ is not a polynomial:
In this case unfortunately, this will be a case by case solving.
Remember that as we have seen in (II.) this will involve summing $\sum f(n)\left(\frac ar\right)^n$ which may or may not have a closed form depending on $f$.
If you have $RHS=\ln(n)$ for instance then you are out of luck, because this does not have a closed form.
On the other hand if $f(n)=\cos(n)$ then you are happy to transform it to $\frac 12(e^{in}+e^{-in})$ and this is a $P(n)a^n$.
In fact especially for this case we can extend the search for particular solutions to $$Q_1(n)\cos(n)+Q_2(n)\sin(n)$$
Where the polynomials $Q_1,Q_2$ follow exactly the same rules than the regular case (with real roots).
V. Some examples and applications:
The Fibonacci sequence:
Let's find a closed for the famous Fibonacci sequences, it verifies $\begin{cases}F_0=0\\F_1=1\\F_n=F_{n-1}+F_{n-2}\end{cases}$
It's characteristic equation is $\,r^2-r-1=0\iff r=\dfrac{1\pm\sqrt{5}}2$, the roots are generally noted $\varphi,\bar \varphi$ where $\varphi$ is known as the golden ratio.
The solution is then $$F_n=a\,\varphi^n+b\,\bar\varphi^n$$
Applying initial conditions gives $\begin{cases}F_0=a+b=0\\F_1=a\varphi+b\bar\varphi=1\end{cases}\iff a=-b=\frac 1{\sqrt{5}}$
$$F_n=\frac 1{\sqrt{5}}\Big(\frac{1+\sqrt{5}}2\Big)^n-\frac 1{\sqrt{5}}\Big(\frac{1-\sqrt{5}}2\Big)^n$$
Lucas numbers:
The Lucas numbers are closely related to Fibonacci numbers, the only difference is in the initial conditions, $L_0=2$ and $L_1=1$.
It solves exactly the same and we get $a=b=1$ instead.
$$L_n=\varphi^n+\bar\varphi^n=\Big(\frac{1+\sqrt{5}}2\Big)^n+\Big(\frac{1-\sqrt{5}}2\Big)^n$$
Pisot numbers whose powers are almost integers:
When dealing with 2-dimensional problems like this one with two conjugated roots (for the square root) and one of the root has modulus $|\bar\varphi|<1$, we call these Pisot numbers, they are remarkable by the fact that their powers get quickly closer and closer to integer values.
Indeed since the modulus is strictly lower than $1$ then $|\bar\varphi|^n\to 0$
Since the Lucas numbers are integers (initial terms are integers and the next term is a sum of integers by induction) then $L_n\sim \varphi^n$.
Similarly the Fibonacci number $F_n$ can be found to be the rounded value of $\left[\dfrac{\varphi^n}{\sqrt{5}}\right]$
Calculating $x^n+y^n$:
A direct application of linear induction relations is calculating $x^n+y^n$ whenever $S=x+y$ and $P=xy$ are given.
Since $x$ and $y$ are solutions of the sum and product quadratic polynomial,
$$r^2-Sr+P=0$$
Considering it as the characteristic equation for a sequence $u_n$, then the sequence must verify
$$u_n=S\,u_{n-1}-P\,u_{n-2}\iff u_n=x^n+y^n$$
Since initial terms are known $\begin{cases}u_0=x^0+y^0=1+1=2\\u_1=x^1+y^1=x+y=S\end{cases}$
Then it is just a direct application of the induction relation which is needed to calculate $u_n$.
Example:
Processing the binomial expansion would surely work, but it would be tedious and prone to calculation and sign errors.
Instead let's call $x=3+2\sqrt{2}$, notice that $y=3-2\sqrt{2}=\dfrac 1x$ therefore $S=x+y=6$ and $P=xy=1$.
$\begin{array}{l} u_0=2\\ u_1=S=6\\ u_2=Su_1-Pu_0=6\times 6-2=34\\ u_3=Su_2-Pu_1=6\times 34-6=198\\ u_4=Su_3-Pu_2=6\times 198-34=1154 \end{array}$
Notice that since $u_0,u_1,S,P$ are all integers, then $u_n$ is always an integer despite its initial looking.
An example with a bit of everything:
Let's dive in a more complex example with multiplicities, complex roots and colliding RHS.
The characteristic polynomial is
$$x^6-2x^4+8x^3-27x^2+32x-12=(x-2i)(x+2i)(x-1)^3(x+3)=0$$
$\pm2i=2\exp(\pm i\frac\pi2)$ are roots, so we will have terms in $2^n(a\sin(\frac{n\pi}2)+b\cos(\frac{n\pi}2))$, and since RHS contains such a term of degree $0$ we will have to search for a particular solution of degree $1$. By the refinement option we can search for $n\,2^n(\alpha\sin(\frac{n\pi}2)+\beta \cos(\frac{n\pi}2))$.
$1$ is root of multiplicity $3$ so we will have a term in $(cn^2+dn+e)\times 1^n$ and since RHS contains a term $(n-7)\times1^n$ of degree $1$ we will have to search for a particular solution of degree $4$. By the refinement option we can search for $(\gamma n^4+\delta n^3)\times1^n$.
$-3$ is root so will will have terms in $f(-3)^n$, no RHS term here.
finally RHS contains $5^n$ but since $5$ is not a root of the characteristic equation we will only have to search for a particular solution of degree $0$, namely $\epsilon\ 5^n$.
To summarize:
$$h_n=2^n\Big(a\sin(\tfrac{n\pi}2)+b\cos(\tfrac{n\pi}2)\Big)+\Big(cn^2+dn+e\Big)+f(-3)^n$$
$$\pi_n=n\,2^n\Big(\alpha\sin(\tfrac{n\pi}2)+\beta\cos(\tfrac{n\pi}2)\Big)+(\gamma n^4+\delta n^3)+\epsilon\,5^n$$
Reporting $\pi_n$ in the recurrence relation gives
$2^n\Big((-296\beta+128\alpha)\sin(\frac{n\pi}2)+(-296\alpha-128\beta)\cos(\frac{n\pi}2)\Big)+\Big(480\gamma\,n+120\delta+1032\gamma\Big)+14848\,\epsilon\,5^n = 2^n\cos(\frac{n\pi}2)+(n-7)+5^n$
And we have to solve the system
$\begin{cases} -296\beta+128\alpha = 0\\ -296\alpha-128\beta=1\\ 480\gamma = 1\\ 120\delta+1032\gamma = -7\\ 14848\,\epsilon = 1 \end{cases} \iff \begin{cases} \alpha = \frac{-37}{13000}\\ \beta= \frac{-2}{1625}\\ \gamma = \frac {1}{480}\\ \delta = \frac {-61}{800}\\ \epsilon = \frac {1}{14848}\\ \end{cases}$
VI. Theoretical justification of $\operatorname{Span}({r_i}^n)$:
The $2$-dimensional problem:
Let $r_1$ and $r_2$ be the roots of the characteristic equation, then they verify the sum and product quadratic,
$$x^2-(r_1+r_2)x+r_1r_2=0$$
Let take advantage of this formulation to transform the $2$-dimensional problem into two $1$-dimensional problems, proceeding like below,
$u_{n+2}-(r_1+r_2)\,u_{n+1}+r_1r_2\,u_n=0\iff \underbrace{\big(u_{n+2}-r_1\,u_{n+1}\big)}_{v_{n+1}}-r_2\underbrace{\big(u_{n+1}-r_1\,u_n\big)}_{v_n}=0$
We get the system: $$\begin{cases}v_{n+1}-r_2\,v_n=0\\u_{n+1}-r_1\,u_n=v_n\end{cases}$$
The first one is homogeneous and solves to $v_n=(r_2)^n\,v_0$
For the second one with RHS we introduce $u_n=(r_1)^n\,U_n$ and we have seen in (II.) that it solves to
$$U_n=U_0+\sum\limits_{i=0}^{n-1}\dfrac{v_i}{{r_1}^i}=U_0+v_0\sum\limits_{i=0}^{n-1}\left(\dfrac{r_2}{r_1}\right)^i$$
We are now facing a condition:
if $r_1=r_2$ (the root has multiplicity $2$) then $\sum\limits_{i=0}^{n-1}\left(\dfrac{r_2}{r_1}\right)^i=\sum\limits_{i=0}^{n-1} 1=n$
if $r_1\neq r_2$ then $\sum\limits_{i=0}^{n-1}\left(\dfrac{r_2}{r_1}\right)^i=\dfrac{r_1}{r_2-r_1}\Big(\Big(\dfrac{r_2}{r_1}\Big)^n-1\Big)$
Gathering the results and regrouping the constant expressions involving $\,U_0,v_0,\frac{r_1}{r_1-r_2}\,$ under arbitrary constants $a,b$ we get:
The $3$-dimensional problem:
Similarly when there are three roots $r_1,r_2,r_3$ we can transform the $3$-dimensional problem in three $1$-dimensional problems by expanding the characteristic equation as below,
$$(x-r_1)(x-r_2)(x-r_3)=x^3-(r_1+r_2+r_3)x^2+(r_1r_2+r_2r_3+r_3r_1)x-r_1r_2r_3$$
It leads to the system:
$$\begin{cases}w_{n+1}-r_3\,w_n=0\\v_{n+1}-r_2\,v_n=w_n\\u_{n+1}-r_1\,u_n=v_n\end{cases}$$
I do not detail all the calculations but similarly when $r$ is a triple root then where $\sum 1$ appeared in the previous case, then now $\sum i$ appears and we get terms in $n^2$.
Generalisation, Newton's identities:
The $n$-dimensional problem is no different, using Newton's identities which give some relations between the roots and the coefficients of the characteristic polynomial, we can transform the $n$-dimensional problem into a system of $1$-dimensional problems.
While it is quite straightforward to establish the above mentioned system, dealing with multiple roots may reveal a bit tedious (but still feasible I guess...). Anyways it follows exactly the same pattern as in the $2$ and $3$-dimensional cases.
This is the reason why, I present in the next paragraph an alternative method which is often preferred for solving the general case.
Solving with linear algebra:
We will change the notations a bit, instead of $\ \sum\limits_{k=0}^p a_k\,x_{n+k}=0\ $, let's use $\ c_i=-\dfrac {a_i}{a_p}$ to get
$$x_{n+p}=\sum\limits_{k=0}^{p-1} c_k\,x_{n+k}$$
So we can write it in matrix form:
$$\begin{bmatrix}x_{n+p}\\x_{n+p-1}\\\vdots\\x_{n+1}\end{bmatrix} = \begin{bmatrix} c_{p-1}&c_{p-2}&\cdots&c_0\\ 1&&\LARGE 0\\ &\ddots\\ \LARGE 0&&1&0\\ \end{bmatrix} \begin{bmatrix}x_{n+p-1}\\x_{n+p-2}\\\vdots\\x_{n}\end{bmatrix}\iff X_{n+1}=C\, X_n$$
And the solution is obviously $X_n=C^n\, X_0$, what is less obvious of course if what does $C^n$ looks like ?
If we assume $C$ is diagonalisable with all distinct eigenvalues, $C=\operatorname{diag}(r_1,r_2,\cdots,r_p)$.
Then $C^n=\operatorname{diag}({r_1}^n,{r_2}^n,\cdots,{r_p}^n)$ and after multiplication with initial terms $X_0$
We end up with the solution we already know $$\sum\limits_{i=0}^{p-1} X_{0,i}\, {r_i}^n$$
But the matrix does not necessarily have distinct eigenvalues, these can have various multiplicities $m_i$.
In this case, we can put the matrix in Jordan form and we have formulas to get the power of a matrix in its Jordan form.
Remember that the basis for polynomials of degree $m$ is not unique.
Therefore all these binomial coefficients you can see in the $J_{m_i}^n(r_i)$ in the link I provided above, just lead to the form discovered in the previous paragraph:
$$(\alpha_0+\alpha_1n+\cdots+\alpha_{m-1}n^{m-1}){r_i}^n$$