How to construct a closed form formula for a recursive sequence? In the Wikipedia page of the Fibonacci sequence, I found the following statement:

Like every sequence defined by a linear recurrence with linear
  coefficients, the Fibonacci numbers have a closed form solution.

The closed form expression of the Fibonacci sequence is:

Another example, from this question, is this recursive sequence:

which has the following closed form formula:

Yet another example from this question is this recursive sequence:

which has the following closed form formula:

So, my question is, how does one come up with these formulae?
Verifying whether a formula is correct or not is easy - that's not what I am asking. I want to know how to come up with a closed form formula for a given recursive sequence.
For example, say, I am interested in the following sequence:
$a_{n+1}$ = $a_n$ + (sum of the digits of $a_n$)
How do I come up with a closed form expression for the $n^{th}$ term of this sequence?
I guess the first step would be to confirm if this sequence is "defined by a linear recurrence with linear coefficients"; if yes, it must have a closed form formula.
 A: 
how does one come up with these formulae?

There is no generic rule that could cover all imaginable recurrences, however there are specific types of recurrences for which one can work out solutions.
One such case where a formula can be given is the linear case (like with Fibonacci numbers), that can be approached by linear algebra: Suppose the recurrence has the form
$$
x_n = a_1 x_{n-1} + a_2 x_{n-2} +\cdots +a_k x_{n-k} = \sum_{j=1}^k a_j x_{n-j}
$$
for $n>k\geqslant 1$ where $x_1$, ..., $x_k$ are given numbers in some field $K$ and the $a_i$ are constants not depending on $n$.
To get an explicit representation for $x_n$, write the recurrence as:
$$
\underbrace{\left(\begin{array}{l}
x_{n\;\;\;}\\
x_{n-1}\\
\;\;\vdots\\
x_{n-k+2}\\
x_{n-k+1}\\
\end{array}\right)}_{\displaystyle{=:y_n}}
=\underbrace{\begin{pmatrix}
a_1 & a_2 & \cdots & a_{k-1} & a_k \\
  1 &   0 & \cdots & 0 & 0 \\
  0 &   1 & \cdots & 0 & 0 \\
  \vdots &&\ddots&&\vdots \\
  0 &   0 & \cdots & 1 & 0 \\
\end{pmatrix}}_{\displaystyle{=:A\in K^{k\times k}}}
\cdot
\underbrace{\left(\begin{array}{l}
x_{n-1}\\
x_{n-2}\\
\;\;\vdots\\
x_{n-k+1}\\
x_{n-k}\\
\end{array}\right)}_{\displaystyle{=:y_{n-1}}}
$$
so that it takes the form
$$
y_n = Ay_{n-1} = A^{n-k}y_k
$$
Hence we'll are left with determining $n$-th powers of a square matrix $A$. Now suppose $A$ has $k$ different eigenvectors $v_j$ and we know all of them, including the corresponding eigenvalues $\lambda_j$.
Then we can write:
$$
y_k = \sum_{j=1}^k \beta_j v_j
= V\begin{pmatrix}
\beta_k\\
\vdots\\
\beta_1\end{pmatrix}
= \begin{pmatrix}v_k&\cdots&v_1\end{pmatrix}\cdot\begin{pmatrix}
\beta_k\\
\vdots\\
\beta_1\end{pmatrix}
$$
where the $\beta_j$ are scalars in the algebraic closure of $K$ and $V$ is a matrix with the eigenvectors of $A$ as columns.
Hence:
$$
y_n = A^{n-k}y_k
= A^{n-k}\Big(\sum_{j=1}^k \beta_j v_j\Big)
= \sum_{j=1}^k \beta_j A^{n-k}v_j
= \sum_{j=1}^k \beta_j \lambda_j^{n-k}v_j \qquad (1)
$$
which leaves is with the computation of the $\beta_j$, the $v_j$ and the $\lambda_j$.
Once we determined the eigenvectors, we get the $\beta_j$ by means of:
$$
\begin{pmatrix}
\beta_k\\
\vdots\\
\beta_1\end{pmatrix}
= V^{-1}y_k
$$
Expanding the determinant of $A-\lambda E$ by expanding after it's top row, we find that all eigenvalues satisfy the characteristic equation
$$\lambda^k = \sum_{j=1}^k a_j\lambda^{k-j} = a_1\lambda^{k-1}+a_2\lambda^{k-2}+\cdots+a_{k-1}\lambda+a_k$$
From this we easily see that the eigenvectors of $A$ are:
$$v_j
= \left(\begin{array}{l}
\lambda_j^{k-1} \\
\;\;\vdots\\
\lambda_j^2 \\
\lambda_j \\
1 \\
\end{array}\right)
$$
Due to (1), in order to get $x_n$ we take the top component of $y_n$ to get:
$$
x_n = \sum_{j=1}^k \beta_j \lambda_j^{n-k}\lambda_j^{k-1}
 = \sum_{j=1}^k \beta_j \lambda_j^{n-1} \qquad (2)
$$
Thus we are finished: Depending on the $a_j$, the eigenvalues can be computed explicitly or by numerical methods.
From the eigenvalues we get the Vandermonde-like matrix $V$ which we use to compute the coefficients $\beta_j$ from the
starting values $x_1$ ... $x_k$ so that we have determined all unknowns in (2).
2nd Order
This is the case $x_n = a_1x_{n-1}+a_2x_{n-2}$.
The matrix $V$ composed of the eigenvectors is:
$V=\begin{pmatrix}
\lambda_2 & \lambda_1\\
1&1\\
\end{pmatrix}$ with inverse
$$
V^{-1}
=\dfrac{1}{\lambda_2-\lambda_1}\begin{pmatrix}
1 & -\lambda_1\\
-1&\lambda_2\\
\end{pmatrix}
$$ so that
$$
\binom{\beta_2}{\beta_1}=\dfrac{1}{\lambda_2-\lambda_1}\binom{x_2-\lambda_1 x_1}{\lambda_2 x_1-x_2}
$$
and we arrive at
$$
x_{n+1}=\dfrac{(\lambda_2 x_1-x_2)\lambda_1^n + (x_2-\lambda_1 x_1)\lambda_2^n}{\lambda_2-\lambda_1}
$$
In the case of Fibonacci numbers, we have $a_1 = a_2 = x_1 = x_2 = 1$.
The characteristic equation is $\lambda^2 = \lambda + 1$ which has the Golden Ratio $\lambda_1=\varphi$ as solution as well as $\lambda_2=\psi=1-\varphi=-1/\varphi$. Plugging in:
$$\begin{align}
x_{n+1}
&=\dfrac{(\psi-1)\varphi^n + (1-\varphi)\psi^n}{\psi-\varphi} \\
&=\dfrac{-\varphi^{n+1} + \psi^{n+1}}{\psi-\varphi} \\
&=\dfrac{\varphi^{n+1} - \psi^{n+1}}{\varphi-\psi} \\
\end{align}$$
Coinciding Eigenvalues
An interesting / annoying case is when two or more eigenvalues are the same so that $V$ is not invertible, so that there is no straight forward way to determine the $\beta_i$. In that case we can still arrive at a solution if $K$ supports concepts like continuity. Take for example the 2-dimensional case from above over $\mathbb R$ or $\mathbb C$ with $\lambda=\lambda_1=\lambda_2$.  We then write $\lambda_2=\lambda+\varepsilon$ and take $\lim_{\varepsilon\to0}$:
$$\begin{align}
x_{n+1}
&=\lim_{\varepsilon\to0}
\dfrac{(\lambda_2 x_1-x_2)\lambda_1^n + (x_2-\lambda_1 x_1)\lambda_2^n}{\lambda_2-\lambda_1}\\
&=\lim_{\varepsilon\to0}
\dfrac{x_1 \lambda (\lambda+\varepsilon) (\lambda^{n-1}-(\lambda+\varepsilon)^{n-1}) + x_2 ((\lambda+\varepsilon)^n - \lambda^n)}{\varepsilon}\\
&= -(n-1)\lambda^n x_1 + n\lambda^{n-1} x_2\\
\end{align}$$
Example: Take $x_{n+2} = 2x_{n+1} - x_n$ with characteristic polynomial $\lambda^2=2\lambda-1$. This has a double root at $\lambda=1$. The explicit formula is hence $x_{n+1}=n x_2 - (n-1)x_1$.
A: Consider 
$$a_{n+1}=a_n+(a_n\bmod 10)$$
which is a simplified version, where you only add the last digit.
By experimentation, except for the first term we have the expression
$$a_n=a_0+5n+\alpha_{a_0}+\beta_{n\bmod 4}$$ where $\alpha_{a_0}$ is a small integer and $\beta_{k}=(1,-3,-1,3)$.
For instance, $7$ yields the sequence of $\delta$'s 
$$2,4,3,6,7,4,3,6,7,4,3,6,7,\cdots$$
There are two special cases: initial number ending in $0$ and in $5$.
The period $4$ is explained as follows: as you add to the number its last digit, this digit gets doubled. And the sequence of doublings modulo $10$ is either $0,0,0,\cdots$ or $2,4,8,6,2,4,8,6,\cdots$. Then the term $5n$ is simply explained by the fact that the average of a period is $5$.

Chances are high that handling the next digits will be pain in the neck, because of the carries.
A: 
For example, say, I am interested in the following sequence:
  $$a_{n+1} = a_n + (\text{sum of the digits of } a_n)$$
  How do I come up with a closed form expression for the $n$th
  term of this sequence?

One could try to look at it modulo some integer number(s).  This won't give an explicit formula, but can give hints, and it can be used to check a potential explicit candidate.
Let$\def\mod{\operatorname{mod}}$
$Q_b(m)$ denote the digit-sum of $m\in\mathbb N_0$ when $m$ is represented to basis $b$.  Then $$Q_b(m) \equiv m \pmod{b-1}$$
This is simply because $b\equiv1\pmod{b-1}$ and the powers $b^k$ in the representation of $m$ will all be $1^k=1$. In the decimal case $b=10$ we have:
$$\begin{align}
a_{n+1}
&= a_n + (\text{sum of the digits of } a_n) \\
&= a_n + Q_{10}(a_n) \\
&\equiv 2a_n \equiv 2^n a_0\pmod 9
\end{align}$$
For example, with $a_0=5$ we get the sequence 5, 10, 11, 13, 17, 25, 32, 37, ... which is mod 9: 5, 1, 2, 4, 8=17, 16=25, 32, 64=37=1, ...
The pattern will be periodic with period 6 because $2^{n\mod6}\equiv2^n\pmod 9$. To see the repetition in the example above, notice that 32=5 mod 9.
For example, $a_{100} \equiv 2^{100} a_0 \equiv 2^4a_0 \equiv 7a_0$, and indeed with $a_0=3$ we have: $a_{100}=1164 \equiv 3 \equiv 7\cdot 3 \pmod 9$.
And we get the following nice properties:


*

*3 divides $a_n$ $\quad\Leftrightarrow\quad$ 3 divides $a_0$.

*9 divides $a_n$ $\quad\Leftrightarrow\quad$ 9 divides $a_0$.
As there is nothing interesting to say about $a_0=0$, let $a_0 \geqslant1$. Then $Q\geqslant1$ and thus the sequence is strictly increasing. Together with the divisibility property from above we get at least linear growth:


*

*$d$ divides $a_0$ $\quad\Rightarrow\quad$ $a_{n+1} -a_n \geqslant d$ for $d\in\{1, 3,9\} $.


Investigating growth will not lead to an explicit formula, but it can help to get a better understanding. So let's get an upper bound: Let $m$ be a $k$-digit number. We then have:
$$
Q_b(m)
\leqslant k(b-1)  
<kb = (1+\lfloor\log_b m\rfloor)b  \leqslant (1+\log_b m)b
$$
so that the growth is at least linear, but not much more:


*

*$a_n \in \mathcal{O} (n\log n)$
