# How to obtain $f(x)$, if it is known that $f(f(x))=x^2+x$?

How to get $f(x)$, if we know that $f(f(x))=x^2+x$?

Is there an elementary function $f(x)$ that satisfies the equation?

• Well, I don't know if this helps but note that $x^2+x=x(x+1)$ – Joao Aug 28 '14 at 8:27
• @joao then?.. still i couldn't find f(x).. – user694345 Aug 28 '14 at 8:33
• This might help : math.stackexchange.com/questions/481017/… – pitchounet Aug 28 '14 at 8:35
• Your problem is finding the half iterate of $x^2+x$. Here is a similar question addressing $x^2+c$ – Ali Caglayan Aug 28 '14 at 10:04
• What are the domain and codomain? I guess $\mathbb R\to \mathbb R$, but it could be $\mathbb Z\to \mathbb Z$. – Ragnar Aug 28 '14 at 10:54

EDDIITT: pretty good approximation ( for $x>4,$ say) with $$\color{blue}{ h(x) \approx x^{\sqrt 2} + \frac{x^{ \left(\sqrt 2 - 1 \right)}}{\sqrt 2} + (1 - \sqrt 2 ) }$$ The following things are true: as long as you just want $C^1$ with no hope of extending to the complex numbers, you can do it, this is a theorem in the KCG book, I put some pages as a pdf HERE . I have been curious on one technical point for four years now, smoothness of the real restriction of Ecalle's solution at the fixpoint itself, and just wrote to Prof. Ger, maybe he will write back.

Meanwhile, see https://mathoverflow.net/questions/45608/formal-power-series-convergence and the correct answer at https://mathoverflow.net/questions/45608/formal-power-series-convergence/46765#46765

Your function $x^2 + x$ also has derivative $1$ at the fixpoint $0,$ but is not a Mobius transformation. What this means is that there is a solution for $x \geq 0$ that is real analytic for $x > 0$ and can therefore be extended to a holomorphic function in an open set containing the strictly positive real axis. The technique for doing all this is due to Jean Ecalle at Orsay, about 1973. The specific steps are in the KCG book, especially pages 346-347 and 351-352. All steps are done with formal power series, abbreviated FPS in the book.

Anyway, goes like this: define $$\color{green}{f(x) = \frac{\sqrt{1 + 4 x} - 1}{2} = \frac{2x}{1 + \sqrt{1 + 4 x}} }$$ for $x > -1/4.$ We need to use use rather than the original $x^2 + x$ because we need convergence in the iteration steps.

$$\color{magenta}{f = x - x^2 + 2 x^3 - 5 x^4 + 14 x^5 - 42 x^6 + 132 x^7 - 429 x^8 + 1430 x^9 - 4862 x^{10} + 16796 x^{11} - 58786 x^{12} + 208012 x^{13} - 742900 x^{14} + 2674440 x^{15} + O(x^{16})}$$



$$\color{magenta}{ \frac{d f}{dx} = 1 - 2 x + 6 x^2 - 20 x^3 + 70 x^4 - 252 x^5 + 924 x^6 - 3432 x^7 + 12870 x^8 - 48620 x^9 + 184756 x^{10} - 705432 x^{11} + 2704156 x^{12} - 10400600 x^{13} + 40116600 x^{14} - 155117520 x^{15} + O(x^{16}) }$$

Find several terms in the formal power series for $\lambda(x)$ that solves $$\lambda(f(x)) = f'(x) \lambda(x),$$ or $$\lambda \left( \frac{\sqrt{1 + 4 x} - 1}{2} \right) = \frac{\lambda(x)}{ \sqrt{1 + 4 x}},$$ where the power series for $\lambda(x)$ is required to begin with the first term in the power series of $f(x)$ after the initial $x.$ To emphasize, you find the FPS's as above and perform this step with those FPS; I gradually extend the series for $\lambda,$ one coefficient at a time. $$\color{magenta}{\lambda = - x^2 + x^3 - \frac{3 x^4}{2} + \frac{8 x^5}{3} - \frac{31 x^6}{6} + \frac{157 x^7}{15} - \frac{649 x^8}{30} + \frac{9427 x^9}{210} - \frac{19423 x^{10}}{210} + \frac{6576 x^{11}}{35} - \frac{2627 x^{12}}{7} + \frac{853627 x^{13}}{1155} - \frac{ 2007055 x^{14}}{ 1386} + \frac{3682190 x^{15}}{ 1287} + O(x^{16}))}$$

Next, write several terms for the reciprocal of the series, and use those in $$\frac{1}{\lambda(x)} = \frac{d \alpha(x)}{dx},$$

$$\color{magenta}{ \frac{d \alpha}{dx} = \frac{-1}{x^2} - \frac{1}{x} + \frac{1}{2} - \frac{2x}{3} + \frac{13x^2}{12} - \frac{113x^3}{60}+ \frac{1187x^4}{360} - \frac{1754x^5}{315} + \frac{14569x^6}{1680} - \frac{176017x^7}{15120} + \frac{ 1745717x^8}{151200} - \frac{ 176434x^9}{51975} - \frac{ 147635381x^{10}}{9979200} + \frac{ 3238110769x^{11}}{129729600} + O(x^{12})}$$

Now, formally integrate to find a short series for $\alpha(x)$ that usually includes a single logarithms term and begins with a few negative powers of $x,$ so it is a logarithm term plus a Laurent expansion. This function $\alpha(x)$ satisfies $$\alpha(f(x)) = \alpha(x) + 1.$$

$$\color{magenta}{ \alpha = \frac{1}{x} - \log x + \frac{x}{2} - \frac{x^2}{3} + \frac{13 x^3}{36} - \frac{113 x^4}{240} + \frac{1187x^5}{1800} - \frac{877x^6}{945} + \frac{14569x^7}{11760} - \frac{176017x^8}{120960} + \frac{1745717x^9}{1360800} - \frac{88217x^{10}}{259875} + O(x^{11})}$$

To actually calculate $\alpha(x)$ for some real number $x > 0,$ define $$x_0 = x, x_1 = f(x), \; x_2 = f(x_1), \; \ldots \; x_{n+1} = f(x_n).$$ From the dfining equation for $\alpha,$ we know that $$\alpha(x_n) - n = \alpha(x).$$ Which is very good, because $x_n$ slowly approaches $0,$ and we can find $\alpha(x)$ to arbitrary accuracy with $$\color{magenta}{ \alpha(x) = \lim_{n \rightarrow \infty} \alpha(x_n) - n},$$ where we are using our peculiar Laurent expansion plus logarithm term in the right hand side. We need a second numerical step, which is to have available $\alpha^{-1}(x).$ I did that with ordinary bisection, slow but reliable.

Finally, you were really interested in $$f^{-1}(x) = x^2 + x.$$ By simple substitution, we have $$\alpha(f^{-1}(x)) = \alpha(x) - 1.$$

Define $$\color{blue}{ h(x) = \alpha^{-1}\left( \alpha(x) - \frac{1}{2} \right)},$$ so that $$\alpha(h(x)) = \alpha(x) - \frac{1}{2}.$$ Then $$h(h(x)) = \alpha^{-1}\left( \alpha(h(x)) - \frac{1}{2} \right),$$ $$h(h(x)) = \alpha^{-1}\left( \left( \alpha(x) - \frac{1}{2} \right) - \frac{1}{2} \right) = \alpha^{-1}\left( \alpha(x) - 1 \right) = \alpha^{-1}\left( \alpha(f^{-1}(x)) \right),$$ $$\color{blue}{ h(h(x)) = f^{-1}(x) = x^2 + x}.$$

This really is the right way to do this. It is just lots of work.

Alright, used gp-pari, the combined Laurent series with log is

If it seems convenient one may include a constant term, in the end it changes nothing.

EDIT, Friday August 29. Quicker than I expected, largely because I still had the C++ program for the sine problem and just had a few changes, all the extra tinkering was in numerical stuff, accuracy demands and so on. The half iterate is called $h(x),$ next column $h(h(x))$ which came out very well, error $h(h(x)) - x - x^2$ in final column.

jagy@phobeusjunior:~/old drive/home/jagy/Cplusplus  ./abel_quadratic
x     alpha(x)                  h(x)              h(h(x))             h(h(x)) - x - x^2
0.1   12.34957156437698    0.1047722467597998    0.109999999999924    -7.601475008391654e-14
0.2   6.698404497655632    0.2183212373574808    0.2400000000057361    5.736109365137498e-12
0.3   4.664365697383913    0.3397339639152821    0.3899999999984503    -1.54972923057696e-12
0.4   3.578318349027508    0.4683176837006184    0.5599999999998941    -1.059019544150455e-13
0.5   2.887563844089283    0.6035247351861248    0.7500000000027918    2.791766817722419e-12
0.6   2.402125463031833    0.7449086888908782    0.9600000000071434    7.143410320729904e-12
0.7   2.038235616342387    0.8920969377271455    1.189999999998397    -1.603108759021948e-12
0.8   1.752874096376789    1.044772606289452    1.43999999999838    -1.619762104391326e-12
0.9   1.521526085243185    1.202662081576193    1.710000000007093    7.093339262319309e-12
1   1.329122322128679    1.365526109628094    1.999999999995433    -4.567457523307894e-12
1.1   1.16584868546157    1.533153249918291    2.309999999999778    -2.227196291976208e-13
1.2   1.025015540899213    1.70535494330933    2.640000000005576    5.575815417019347e-12
1.3   0.9018917080819405    1.881961717365725    2.990000000005553    5.552944822712069e-12
1.4   0.7930276007682336    2.06282021339842    3.359999999999707    -2.924416351440806e-13
1.5   0.6958428672226297    2.247790820476767    3.750000000002591    2.590816450265265e-12
1.6   0.6083648146160752    2.4367457662832    4.16000000000693    6.929337095437638e-12
1.7   0.5290566936740566    2.629567557533946    4.590000000005444    5.444587055508654e-12
1.8   0.4567016007712204    2.826147692171223    5.039999999998141    -1.859126134290401e-12
1.9   0.3903219460842974    3.026385585323028    5.509999999992289    -7.710951885689377e-12
2   0.3291223221286791    3.230187665709464    6.000000000002442    2.442490654175344e-12
2.1   0.2724481590078001    3.437466609237211    6.509999999999497    -5.033837929824259e-13
2.2   0.2197552692216855    3.648140684345403    7.040000000012558    1.255703093588911e-11
2.3   0.1705870545953422    3.862133188737511    7.589999999990695    -9.303704508190069e-12
2.4   0.1245572014479078    4.079371962336136    8.159999999999389    -6.104092925562909e-13
2.5   0.08133637076430866    4.299788963036583    8.75000000000226    2.259525899717119e-12
2.6   0.04064183929403394    4.523319895822285    9.360000000006586    6.585612263854124e-12
2.7   0.00222934965743236    4.749903886620089    9.98999999999781    -2.191171723231466e-12
2.8   -0.0341133655737727    4.97948319436885    10.63999999999049    -9.508873723140798e-12
2.9   -0.068571776927252    5.212002955640676    11.30999999999917    -8.249043809138712e-13
3     -0.1013087576914362    5.447410957318214    12.00000000000204    2.042810365310288e-12
3.1   -0.1324680069216879    5.685657433479092    12.71000000000636    6.363123569719242e-12
3.2   -0.1621768598101268    5.926694883278735    13.43999999999758    -2.422986290773199e-12
3.3   -0.190548610778305    6.170477907124946    14.19000000000481    4.809450580844921e-12
3.4   -0.2176844459848682    6.416963058866861    14.95999999999166    -8.343778792885281e-12
3.5   -0.2436750604884958    6.666108712048336    15.75000000000179    1.794120407794253e-12
3.6   -0.2686020190760892    6.917874938523173    16.56000000002793    2.793298058134663e-11
3.7   -0.2925389073885415    7.172223397984279    17.38999999999731    -2.686775281424136e-12
3.8   -0.3155523105139151    7.42911723765007    18.23999999999727    -2.732072380828843e-12
3.9   -0.3377026486746939    7.688520999656106    19.10999999999866    -1.342712399599044e-12
4     -0.3590448941611992    7.950400537177861    20.00000000001606    1.605826582817826e-11
4.1   -0.3796291888415436    8.214722936869059    20.91000000001309    1.309516037273362e-11
4.2   -0.3995013781846659    8.481456447739166    21.83999999999703    -2.975432400464939e-12
4.3   -0.4187034747973836    8.750570415524315    22.78999999998968    -1.031622703928647e-11
4.4   -0.4372740622184186    9.022035222085844    23.75999999999835    -1.653930464806663e-12
4.5   -0.4552486478563347    9.295822229429085    24.75000000000846    8.462563982902793e-12
4.6   -0.4726599724737711    9.571903727745777    25.76000000000549    5.494129456939945e-12
4.7   -0.4895382824037881    9.850252887298186    26.78999999996759    -3.241055340774679e-11
4.8   -0.5059115696867305    10.13084371362828    27.83999999999663    -3.369783618811795e-12
4.9   -0.5218057845044439    10.41365100589565    28.9100000000271    2.709995769456519e-11
5     -0.5372450236123233    10.69865031803113    30.00000000000084    8.384404281969182e-13
5.1   -0.5522516979121136    10.98581792253899    31.11000000000513    5.133529018541694e-12
5.2   -0.5668466818374271    11.2751307765618    32.2399999999745    -2.550141348089952e-11
5.3   -0.5810494468467916    11.56656649026647    33.39000000001808    1.807883703852653e-11
5.4   -0.5948781809801713    11.86010329704915    34.55999999999035    -9.651088955786591e-12
5.5   -0.6083498961731976    12.15572002578478    35.7499999999859    -1.409716787748039e-11
5.6   -0.6214805247781624    12.45339607461181    36.95999999999748    -2.517239888755185e-12
5.7   -0.6342850065582102    12.75311138636128    38.1900000000032    3.192966724352431e-12
5.8   -0.6467773672439603    13.05484642542658    39.43999999999583    -4.163815126023707e-12
5.9   -0.6589707896064629    13.35858215597922    40.7100000000045    4.494040556357604e-12
6     -0.670877677871321    13.66430002140698    42.00000000001459    1.459454779251246e-11
6.1   -0.6825097162058276    13.97198192491624    43.30999999998249    -1.75008098290963e-11
6.2   -0.6938779219064769    14.28161021123291    44.64000000003188    3.187313607488917e-11
6.3   -0.7049926938537755    14.59316764924931    45.9900000000318    3.180119362289346e-11
6.4   -0.715863856716369    14.90663741565324    47.36000000002589    2.588670597325482e-11
6.5   -0.726500701345836    15.222003079372    48.7499999999996    -3.979039320256561e-13
6.6   -0.7369120217414309    15.53924858691136    50.15999999993838    -6.161663193560152e-11
6.7   -0.7471061489219293    15.85835824833476    51.58999999999504    -4.958734811655319e-12
6.8   -0.7570909820130148    16.17931672400653    53.03999999998766    -1.234038565778306e-11
6.9   -0.7668740168092776    16.50210901226642    54.51000000100765    1.007648933043503e-09
7     -0.7764623720559678    16.82672043658028    55.99999999995545    -4.455102953215828e-11
7.1   -0.7858628136599484    17.15313663528092    57.50999999983604    -1.639543258102893e-10
7.2   -0.7950817770212208    17.48134355022496    59.04000000001636    1.636021179640679e-11
7.3   -0.804125387653983    17.81132741620624    60.58999999998716    -1.284043010807423e-11
7.4   -0.8129994802545112    18.14307475151896    62.15999999998124    -1.876735225558868e-11
7.5   -0.8217096163452652    18.47657234830246    63.74999999999861    -1.392663762089796e-12
7.6   -0.8302611006262054    18.81180726364275    65.3599999999301    -6.989800752088549e-11
7.7   -0.8386589961406261    19.14876681105959    66.98999999999401    -5.990798135346864e-12
7.8   -0.8469081383553058    19.487438552212    68.64000000004481    4.48103429362412e-11
7.9   -0.8550131482496721    19.82781028913469    70.30999999998798    -1.202607805006473e-11
8    -0.8629784444906156    20.16987005676574    71.99999999999801    -1.989519660128281e-12
8.1   -0.870808254770019    20.51360611569972    73.71000000008232    8.232334258728713e-11
8.2   -0.8785066263741219    20.8590069453774    75.43999999996436    -3.563073447399034e-11
8.3   -0.8860774360427158    21.20606123751556    77.18999999997149    -2.852175440271054e-11
8.4   -0.8935243991735187    21.55475788971871    78.9599999999146    -8.540559925940272e-11
8.5   -0.9008510784275988    21.90508599953461    80.75000000001202    1.20223830890609e-11
8.6   -0.9080608917744842    22.25703485853165    82.55999999995808    -4.191327840352699e-11
8.7   -0.9151571200258515    22.61059394681281    84.38999999998555    -1.443879737994536e-11
8.8   -0.9221429138912409    22.96575292760232    86.23999999998546    -1.455603693134577e-11
8.9   -0.9290213005950486    23.32250164214523    88.10999999992129    -7.872116847273958e-11
9    -0.9357951900840553    23.68083010472332    90.00000000001143    1.142552719102241e-11
9.1   -0.9424673808558298    24.04072849786763    91.9099999999938    -6.192518720027351e-12
9.2   -0.94904056543564    24.40218716784729    93.83999999989763    -1.023578027892214e-10
9.3   -0.9555173355278145    24.76519662026029    95.79000000002119    2.118070996370847e-11
9.4   -0.9619001868602525    25.12974751565342    97.7599999999716    -2.840941371040628e-11
9.5   -0.9681915237489729    25.49583066562954    99.74999999998167    -1.833200258261058e-11
9.6   -0.9743936633963083    25.86343702875129    101.7599999999859    -1.412750472162827e-11
9.7   -0.980508839945976    26.23255770679393    103.7900000000061    6.117203965594342e-12
9.8   -0.9865392083063443    26.60318394112198    105.8399999999696    -3.039403451143841e-11
9.9   -0.9924868477624008    26.97530710907296    107.9099999999854    -1.456271214728133e-11
10   -0.9983537653840405    27.34891872058871    109.9999999999955    -4.462208380573429e-12
x     alpha(x)                  h(x)              h(h(x))             h(h(x)) - x - x^2
jagy@phobeusjunior:~/old drive/home/jagy/Cplusplus


• September 16 2014: I wrote to Prof. Ecalle, it turns out that the (real) solutions constructed in this way really are $C^\infty,$ as well as being $C^\omega$ except at the fixpoint. Note that I did not do the calculation to the left of the origin, in that case we use $x^2 + x$ itself rather than the inverse function, the mildly bad news is that I am not sure how far the basin of attraction extends along the negative real line. Almost certainly as far as $-1/2;$ I would say the best possible is $-1,$ as we need the iterated values to stay negative. – Will Jagy Sep 16 '14 at 19:27
• Will, I've just seen, that your $\lambda()$-function down to the Abel-function $\alpha()$ can directly be determined by the use of the log of the Carlemanmatrix F for the function $f(x)$ . I did never know that this is such an easy relation between that two computation-concepts. See the end of my long answer. – Gottfried Helms Jun 17 '15 at 17:29

There is no such $f:\mathbb C\to\mathbb C$. See this paper:

When is $f(f(z)) = az^2 + bz + c$ ?
by R. E. Rice, B. Schweizer and A. Sklar
The American Mathematical Monthly, vol. 87, no. 4 (Apr., 1980), pp. 252â€“263

More generally, they prove that a quadratic polynomial has no iterative roots of any order.

• Adapted from math.stackexchange.com/a/483814/589. – lhf Aug 28 '14 at 11:07
• Do you mean a polynomial $f$ or a function $f$? – Ali Caglayan Aug 28 '14 at 11:47
• As you say, that paper proves that there is no such function over $\mathbb C$. The paper has an Epilogue section, in which they prove that subject to certain conditions (which I believe are satisfied here), there is such a function over $\mathbb R$. – TonyK Aug 28 '14 at 12:11
• @TonyK, there is a function over $\mathbb R$ which has a holomorphic extension in an open set around the strictly positive real axis, and another extension around the negative real axis; the two cannot be joined up. – Will Jagy Aug 28 '14 at 18:01
• I think you should add the existence over the reals to the answer. as it stands, it's very misleading. – nbubis Aug 29 '14 at 19:50

Hereâ€™s a technique for finding the first few terms of a formal power series representing the fractional iterate of a given function like $f(x)=x+x^2$. I repeat that this is a formal solution to the problem, and leaves unaddressed all considerations of convergence of the series answer.

Iâ€™m going to find the first six terms of $f^{\circ1/2}(x)$, the â€śhalf-thâ€ť iterate of $f$, out to the $x^5$-term. Letâ€™s write down the iterates of $f$, starting with the zero-th. \begin{align} f^{\circ0}(x)&=x\\ f^{\circ1}=f&=x&+x^2\\ f^{\circ2}&=x&+2x^2&+2x^3&+x^4\\ f^{\circ3}&\equiv x&+3x^3&+6x^3& + 9x^4& + 10x^5& + 8x^6\\ f^{\circ4}&\equiv x &+ 4x^2& + 12x^3& + 30x^4& + 64x^5& + 118x^6\\ f^{\circ5}&\equiv x& + 5x^2& + 20x^3& + 70x^4& + 220x^5& + 630x^6\\ f^{\circ6}&\equiv x& + 6x^2& + 30x^3& + 135x^4& + 560x^5& + 2170x^6\\ f^{\circ7}&\equiv x& + 7x^2& + 42x^3& + 231x^4& + 1190x^5& + 5810x^6\,, \end{align} where the congruences are modulo all terms of degree $7$ and more.

Now look at the coefficients of the $x$-term: always $1$. Of the $x^2$-term? In $f^{\circ n}$, itâ€™s $C_2(n)=n$. The coefficient of $x^3$ in $f^{\circ n}$ is $C_3(n)=n(n-1)=n^2-n$, as one can see by inspection. Now, a momentâ€™s thought (well, maybe several momentsâ€™) tells you that $C_j(n)$, the coefficient of $x^j$ in $f^{\circ n}$, is a polynomial in $n$ of degree $j-1$. And a familiar technique of finite differences shows you that \begin{align} C_4(n)&=\frac{2n^3-5n^2+3n}2\\ C_5(n)&=\frac{3n^4-13n^3+18n^2-8n}3\,, \end{align} I wonâ€™t go into the details of that technique. The upshot is that, modulo terms of degree $6$ and higher, you have $f^{\circ n}(x)\equiv x+nx^2+(n^2-n)x^3+\frac12(2n^3-5n^2+3n)x^4+\frac13(3n^4-13n^3+18n^2-8n)x^5$.

Now, you just plug in $n=\frac12$ in this formula to get your desired series. And Iâ€™ll leave it to you to go one degree higher, using the iterates Iâ€™ve given you.

• If you're interested, see mathoverflow.net/questions/45608/… and especially mathoverflow.net/questions/45608/… as the current question also has derivative $1$ at the fixed point. I still do not know, as I suspect, whether the real half iterate of sine is $C^\infty$ at $0.$ Related articles and book excerpts at zakuski.utsa.edu/~jagy/other.html – Will Jagy Aug 28 '14 at 17:47
• That's nice! I've never seen this trick before. – TonyK Aug 28 '14 at 18:05
• I wrote to Jean Ecalle; when the derivative ( of a real valued function) at the fixpoint is $1,$ as here, the fractional iterates really are $C^\infty$ as real functions, radius of convergence is $0.$ These are then extended as holomorphic in sectors of which the fixpoint is a boundary point, as I put in my answer. – Will Jagy Sep 28 '14 at 18:05

Although the question has already been satisfyingly answered I'd like to add some more general framework for questions like this. For polynomials and power series there is the concept of Bell- and Carleman-matrices.

The basic idea is that if you have a Carleman-matrix $F$ associated to a function $f(x)$ then the function $g(x)=fÂ°^{1/2}(x)$ is associated to (matrix-) squareroot $G=F^{1/2}$ (which is again a Carlemanmatrix).

This plays all around only on the coefficients of the formal power series ("FPS") and the typical cases are that of polynomials and/or power series without constant term: $$\begin{eqnarray} f(x)&=&\sum_{k=1}^N a_k \cdot x^k &\qquad \qquad \text{ or }\\ f(x)&=&\sum_{k=1}^\infty a_k \cdot x^k \end{eqnarray}$$ (but can be generalized)

With some relatively simple very general and standard user-procedures using the software Pari/GP I express your problem simply by

n = 16 \\ setting dimension for matrices and vectors

F = carleman(x+x^2,n)    \\ making F a carlemanmatrix for f(x)

G = SQRT(F,1,1)          \\ matrix-squareroot, flags 1,1 indicate specialized
\\  routine for lower triangular matrices
\\  with units in the diagonal

g = Ser(G[,2]) + O(x^12) \\ extract from G's second column its n coefficients
\\ and write them as FPS with the first 12 terms


getting the leading terms of a formal power series

g(x) =  x + 1/2*x^2 - 1/4*x^3 + 1/4*x^4 - 5/16*x^5 + 27/64*x^6 - 9/16*x^7
+ 171/256*x^8 - 69/128*x^9 - 579/2048*x^10 + 10689/4096*x^11 + O(x^12)


To have a function with another fractional iteration height I use like in the logic of fractional powers of scalars:

n = 16 \\ setting dimension for matrices and vectors
h = 3/4   \\ setting some example fractional iteration-height

F = carleman(x+x^2,n)   \\ making F a carlemanmatrix for f(x)

L = LOG(F,1,1)          \\ matrix-logarithm, flags 1,1 indicate specialized
\\ routine for lower triangular matrices
\\ with units in the diagonal
G = EXP( h * L,1,1)     \\ by the Exponential this gives the fractional
\\ h'th power of a triangular matrix,
\\ flags with the equivalent meaning as in LOG

g = Ser(G[,2]) + O(x^12) \\ extract from G's second column its n coefficients
\\ and write them as FPS with the first 12 terms


giving, for this instance with $h=3/4$

 g(x) = x + 3/4*x^2 - 3/16*x^3 + 9/64*x^4 - 35/256*x^5 + 35/256*x^6
- 449/4096*x^7 - 1/2048*x^8 + 19041/65536*x^9 - 461901/524288*x^10
+ 1870803/1048576*x^11 + O(x^12)


We can get this even symbolically, where $h$ is left indeterminate, all in he same small framework:

n = 16   \\ setting dimension for matrices and vectors
h = 'h   \\ resetting h to its symbolical use

F = carleman(x+x^2,n)   \\ making F a carlemanmatrix for f(x)

L = LOG(F,1,1)          \\ matrix-logarithm, flags 1,1 indicate specialized
\\ efficient routine for lower triangular matrices
\\ with units in the diagonal (exact rational arithmetic
\\ is possible
G = EXP( h * L,1,1)     \\ by the Exponential this gives the fractional h'th
\\ power of a triangular matrix. Same flags
\\ indicate possibility for efficient exact computation

g = Ser(G[,2]) + O(x^n) \\ extract from G's second column its n coefficients
\\ and write them as FPS with the first n terms


giving the two parametric function $g()$ with $x$ , and $h$ for the iteration-height:

 g(x,h)= x
+ h*x^2
+ (h^2 - h)*x^3
+ (h^3 - 5/2*h^2 + 3/2*h)*x^4
+ (h^4 - 13/3*h^3 + 6*h^2 - 8/3*h)*x^5
+      O(x^6)


with the coefficients at the powers of $x$ as polynomials in $h$ as already shown in @Lubin's answer.

While the logic of the frational iteration of (-of course- certain classes of) functions is thus reduced to the simple analogue of scalar fractional powers, there are some formal requirements and conditions to make this really helpful.
For instance, while it is as easy as shown to find the leading terms of a formal power series (as far as $f(x)$ has no constant term) it does not mean, that that resulting power series has a finite convergence radius or a nonzero radius of convergence at all, so in classical terms it might be merely useless. (Sometimes one can apply Euler-, Borel- or Noerlund summation for a divergent series to get approximate results anyway, but this is not the focus here. @Will Jagy's answer above shows a related (and might be even better) way to arrive at approximations)

If the basic function $f(x)$ has still no constant term but a coefficient $a_1 \notin \{0,1\}$ the analogue can be done by diagonalization, so for instance for something like $f(x)=1/2 x + 3/4 x^2 - x^3$ we need tell our LOG and EXP-routine, that they shall do things different instead, and shall use diagonalization and apply the iteration height on the exponents of the eigenvalues to create the fractional power of the Carlemanmatrix G for the desired iteration height. But again: while we'll get the correct leading terms of a power series, in most cases that power series for fractional iteration heights shall have radius of convergence being zero, so it cannot be evaluated for any $x$ - except we

1. a) introduce methods for divergent summation or
2. b) reduce ourselves to the evaluation of the asymptotic formal power series only up to some optimal truncation.

[update Jun 2015]:

Here is a sample-computation, parallel to that of @Will Jagy.

The formal power series for $g(x)$ (=$f(x)$ in the OP and $h(x)$ in Will Jagy's post) is surely only asymptotic (meaning: has convergence radius zero).

But by the functional relation we have $x_{0.5} = ((x_{-20})_{+0.5})_{+20}$ so that we replace $x' = x_{-20}$ which is closer to the fixpoint zero. If we insert this value in the asymptotic formal power series we have after, say, 64 terms a partial sum for $g(x')$ (locally) converging to some value whose thirty or forty digits stay constant. If we truncate the series we have a good estimate (but, due to the characteristic of the series as asymptotic only) cannot much be improved (except we would replace $x' = x_{-40}$ or an even higher iterate) . Then we use the functional equation again to iterate now 20 times $g(x) = g(x')_{+20}$ . That this procedure is a meaningful approximation shows the following table, where the error is already very small (and can be made smaller as is wished).

The table is computed using the iteration of $x$ towards the fixpoint until $x' <0.01$ (call the required number of inverse iterations "height-offset") , then $64$ terms of the FPS are taken, and the result $g(x')$ was iterated away from the fixpoint zero by the same "height offset".

   x                g(x)             g(g(x))       (x+x^2)- g(g(x))
------------------------------------------------------------------
0.100000000000  0.104772246757  0.110000000000  1.77368712134E-89
0.200000000000  0.218321237354  0.240000000000  8.39980200859E-89
0.300000000000  0.339733963915  0.390000000000  2.06063470023E-88
0.400000000000  0.468317683702  0.560000000000  3.01316590953E-88
0.500000000000  0.603524735182  0.750000000000  5.56377802786E-88
0.600000000000  0.744908688889  0.960000000000  8.30936184814E-88
0.700000000000  0.892096937726   1.19000000000  1.08442417625E-87
0.800000000000   1.04477260629   1.44000000000  1.74985488654E-87
0.900000000000   1.20266208158   1.71000000000  1.94506810567E-87
1.00000000000   1.36552610963   2.00000000000  2.78678464389E-87
1.10000000000   1.53315324992   2.31000000000  3.83614094010E-87
1.20000000000   1.70535494330   2.64000000000  3.76726767336E-87
1.30000000000   1.88196171736   2.99000000000  4.88855868818E-87
1.40000000000   2.06282021339   3.36000000000  6.20513270304E-87
1.50000000000   2.24779082048   3.75000000000  7.73029893345E-87
1.60000000000   2.43674576629   4.16000000000  6.98399441754E-87
1.70000000000   2.62956755754   4.59000000000  8.44163571068E-87
1.80000000000   2.82614769218   5.04000000000  1.00801098914E-86
1.90000000000   3.02638558534   5.51000000000  1.19080669499E-86
2.00000000000   3.23018766572   6.00000000000  1.39339232195E-86
2.10000000000   3.43746660925   6.51000000000  1.61658749748E-86
2.20000000000   3.64814068433   7.04000000000  1.86119108535E-86
2.30000000000   3.86213318872   7.59000000000  2.12798232294E-86
2.40000000000   4.07937196232   8.16000000000  2.41772186544E-86
2.50000000000   4.29978896302   8.75000000000  2.01254742998E-86
2.60000000000   4.52331989580   9.36000000000  2.26122901762E-86
2.70000000000   4.74990388660   9.99000000000  2.52837923787E-86
2.80000000000   4.97948319435   10.6400000000  2.81450750374E-86
2.90000000000   5.21200295561   11.3100000000  3.12011260578E-86
3.00000000000   5.44741095729   12.0000000000  3.44568319447E-86
3.10000000000   5.68565743345   12.7100000000  3.79169822947E-86
3.20000000000   5.92669488324   13.4400000000  4.15862739869E-86
3.30000000000   6.17047790709   14.1900000000  4.54693150987E-86
3.40000000000   6.41696305883   14.9600000000  4.95706285700E-86
3.50000000000   6.66610871201   15.7500000000  5.38946556386E-86
3.60000000000   6.91787493848   16.5600000000  5.84457590632E-86
3.70000000000   7.17222339803   17.3900000000  6.32282261544E-86
3.80000000000   7.42911723769   18.2400000000  6.82462716263E-86
3.90000000000   7.68852099970   19.1100000000  7.35040402841E-86
4.00000000000   7.95040053722   20.0000000000  7.90056095603E-86
4.10000000000   8.21472293692   20.9100000000  8.47549919096E-86
4.20000000000   8.48145644779   21.8400000000  6.68807161944E-86
4.30000000000   8.75057041558   22.7900000000  7.14874364065E-86
4.40000000000   9.02203522214   23.7600000000  7.62849696346E-86
4.50000000000   9.29582222949   24.7500000000  8.12760832582E-86
4.60000000000   9.57190372781   25.7600000000  8.64635043990E-86
4.70000000000   9.85025288737   26.7900000000  9.18499212034E-86
4.80000000000   10.1308437137   27.8400000000  9.74379840606E-86
4.90000000000   10.4136510060   28.9100000000  1.03230306763E-85
5.00000000000   10.6986503181   30.0000000000  1.09229467614E-85
5.10000000000   10.9858179226   31.1100000000  1.15438010481E-85
5.20000000000   11.2751307766   32.2400000000  1.21858445800E-85
5.30000000000   11.5665664903   33.3900000000  1.28493251542E-85
5.40000000000   11.8601032971   34.5600000000  1.35344874129E-85
5.50000000000   12.1557200259   35.7500000000  1.42415729315E-85
5.60000000000   12.4533960747   36.9600000000  1.49708203024E-85
5.70000000000   12.7531113865   38.1900000000  1.57224652159E-85
5.80000000000   13.0548464255   39.4400000000  1.64967405377E-85
5.90000000000   13.3585821561   40.7100000000  1.72938763825E-85
6.00000000000   13.6643000215   42.0000000000  1.81141001853E-85
6.10000000000   13.9719819250   43.3100000000  1.89576367700E-85
6.20000000000   14.2816102113   44.6400000000  1.98247084146E-85
6.30000000000   14.5931676494   45.9900000000  2.07155349145E-85
6.40000000000   14.9066374158   47.3600000000  2.16303336434E-85
6.50000000000   15.2220030795   48.7500000000  2.25693196111E-85
6.60000000000   15.5392485870   50.1600000000  2.35327055204E-85
6.70000000000   15.8583582485   51.5900000000  2.45207018209E-85
6.80000000000   16.1793167241   53.0400000000  2.55335167614E-85
6.90000000000   16.5021090122   54.5100000000  2.65713564400E-85
7.00000000000   16.8267204365   56.0000000000  2.76344248533E-85
7.10000000000   17.1531366352   57.5100000000  2.87229239424E-85
7.20000000000   17.4813435501   59.0400000000  2.98370536390E-85
7.30000000000   17.8113274161   60.5900000000  3.09770119088E-85
7.40000000000   18.1430747514   62.1600000000  3.21429947937E-85
7.50000000000   18.4765723482   63.7500000000  3.33351964530E-85
7.60000000000   18.8118072635   65.3600000000  3.45538092028E-85
7.70000000000   19.1487668109   66.9900000000  3.57990235540E-85
7.80000000000   19.4874385520   68.6400000000  3.70710282498E-85
7.90000000000   19.8278102890   70.3100000000  3.83700103014E-85
8.00000000000   20.1698700566   72.0000000000  3.96961550227E-85
8.10000000000   20.5136061155   73.7100000000  4.10496460640E-85
8.20000000000   20.8590069452   75.4400000000  4.24306654448E-85
8.30000000000   21.2060612373   77.1900000000  4.38393935851E-85
8.40000000000   21.5547578895   78.9600000000  3.33657414671E-85
8.50000000000   21.9050859994   80.7500000000  3.44443732837E-85
8.60000000000   22.2570348583   82.5600000000  3.55437811314E-85
8.70000000000   22.6105939466   84.3900000000  3.66640929124E-85
8.80000000000   22.9657529274   86.2400000000  3.78054354602E-85
8.90000000000   23.3225016420   88.1100000000  3.89679345586E-85
9.00000000000   23.6808301045   90.0000000000  4.01517149613E-85
9.10000000000   24.0407284977   91.9100000000  4.13569004096E-85
9.20000000000   24.4021871676   93.8400000000  4.25836136512E-85
9.30000000000   24.7651966200   95.7900000000  4.38319764567E-85
9.40000000000   25.1297475154   97.7600000000  4.51021096373E-85
9.50000000000   25.4958306654   99.7500000000  4.63941330610E-85
9.60000000000   25.8634370285   101.760000000  4.77081656687E-85
9.70000000000   26.2325577066   103.790000000  4.90443254899E-85
9.80000000000   26.6031839409   105.840000000  5.04027296580E-85
9.90000000000   26.9753071088   107.910000000  5.17834944249E-85
10.0000000000   27.3489187203   110.000000000  5.31867351757E-85


It might be interesting, that we can partially reproduce Will Jagy's computation with the resources which we have computed so far.
The $\lambda()$ - function can be taken from the coefficients of the second column of the $\log$-matrix $L$, and then it needs two more steps to arrive at the Abel-function $\alpha()$:

lam = Ser(-L[,2])  \\ the minus-sign indicates that we want the
\\ log of the inverse f(x): log of sqrt(1+x/4)-1/2

lam_rec = 1/lam    \\ Pari/GP allows to compute the formal reciprocal

\\ in the following formal integral the 1/x-term
\\ must be removed as Pari/GP is unable to include
\\ a formal expression for log(x):
alpha = intformal(lam_rec + 1/'x) - logx   \\ lx = log(x)
\\ "logx" means, we must further work with that term


Check it out:

lam = Ser( - L[,2])
%995 = -x^2 + x^3 - 3/2*x^4 + 8/3*x^5 - 31/6*x^6 + 157/15*x^7 - 649/30*x^8 + O(x^9)

lam_rec = 1/lam
%996 = -x^-2 - x^-1 + 1/2 - 2/3*x + 13/12*x^2 - 113/60*x^3 + 1187/360*x^4 - 1754/315*x^5 + O(x^6)

alpha = intformal(lam_rec + 1/'x) - logx   \\ lx = log(x)
%998 = x^-1 - logx + 1/2*x - 1/3*x^2 + 13/36*x^3 - 113/240*x^4 + O(x^5)

• Thanks a lot for the Carleman matrices. – mathreadler Jun 17 '15 at 8:01

Firstly, let $g(x)$ be equal to $x^2+x$. Now we can say that $g(x)=g(y)$ is equivalent to $x=y$ or $x+y=-1$. So $g(g(x))=g(g(y))$ means that $g(x)=g(y)$ or $g(x)+g(y)=-1$. But $g(x)=\left(x+\frac{1}{2}\right)^2-\frac{1}{4}\ge-\frac{1}{4}$, so the second case can't take place. Thus, $g^n(x)=g^n(y)$ iff $g(x)=g(y)$ for every positive integer $n$.

Also $f$ returns each number not less than $-\frac{1}{4}$.

Well, I don't have a full solution and I print it on my little mobile phone, so my idea is the following: all real numbers can be divided onto many infinitive sequences, some of them are also infinitive to the left, every number is a value of $g$ at the previous number in the same sequence (if it exists), and some numbers don't occur in any sequence, but their values of $g$ are in one of our sequences. In other words, we draw an arrow from $x$ to $g(x)$ for every $x$. After all I've written and your imagination we can understand our situation. Now $f$ can just divide there sequences by pairs and map them to each other at every pair. Sorry for not too clear explanation

Well, one more answer, the most q&d trick, giving the formal power series for the half-iterate using the Newton-squareroot-algorithm applied to formal power series. (Thus it is in principle the same logic as the Carleman-ansatz as in my earlier answer but looks stunningly simpler).

In Pari/GP one has the builtin-function "serreverse(f)" finding the reverse of a formal power series (which must not have a constant term as is in your problem).

So we do the following

   Z(x) = x + x^2       \\ define the function of which we want the half-iterate

g = x + O(x^32)  \\ declare g as formal power series as initial "value"
for(k = 2, 7, g = (Z(serreverse(g))+ g)/2 )    \\ just iterate several times

print(g + O(x^9))      \\ correct to the seventh term:


Result:

      x + 1/2*x^2 - 1/4*x^3 + 1/4*x^4 - 5/16*x^5 + 27/64*x^6 - 9/16*x^7
+ 357/512*x^8 + O(x^9)


The eigth term were correct ( 171/256 x^8 ) if we had iterated one more time.

In the same way we would get the formal power series of the half-iterate for the notorious case $g(g(x)) = \exp(x)-1$ just by initializing $Z=exp(x)-1$ in the above code.

If we assume a function which has a power series expansion, we can just assume f to have a Maclaurin polynomial. Then the coefficients of $f(f(x))$ is a linear combination of iterated convolutions, for which the right hand side is $[0,1,1,0,0,\cdots]$. Also some regularization should likely be employed, punishing too large coefficients for large exponent monomials.