I have the following system of equations where the $m$'s are known but $a, b, c, x, y, z$ are unknown. How does one go about solving this system? All the usual linear algebra tricks I know don't apply and I don't want to do it through tedious substitutions.

\begin{aligned} a + b + c &= m_0 \\ ax + by + cz &= m_1 \\ ax^2 + by^2 + cz^2 &= m_2 \\ ax^3 + by^3 + cz^3 &= m_3 \\ ax^4 + by^4 + cz^4 &= m_4 \\ ax^5 + by^5 + cz^5 &= m_5 \end{aligned}

  • 1
    $\begingroup$ If the variables are general reals and there is no special relation between the m's, there ain't no graceful way that I know of. If the variables are integers, tricks involving modular arithmetic can help a lot. Did you get $m_1$ and $m_2$ backwards? $\endgroup$ Dec 5, 2010 at 23:30
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    $\begingroup$ So you know the sum of three geometric progressions, and you want to get the progressions back? I agree with Ross, this could be a thorny problem. $\endgroup$
    – user856
    Dec 5, 2010 at 23:35
  • $\begingroup$ There is no universal way for solving (exactly) a non-linear system of equations. The strategy varies from question to question, and in some cases, you can only use numerical methods to approximate the solution(s). In this case, you have a lot of symetry on the LHS of your equations. Try to take advantage of this. You could subtract one equation from another, then factor the LHS of the resulting equation. You may also want to try multiplying one equation by the term xyz to make it look more like the equation below it. $\endgroup$
    – user3180
    Dec 5, 2010 at 23:37
  • 1
    $\begingroup$ You could type it into a computer algebra system and it will solve it for you using Groebner bases. If you want to find a simple explicit solution, I suggest to try first with only 2 geometric series. $\endgroup$ Dec 5, 2010 at 23:49
  • 1
    $\begingroup$ Since everybody is bringing bad news, here is some good news. With the symmetry of a,b,c and x,y,z, there will be repetition in the solutions. Instead of 120, you will have rather fewer. If x, y, and z are all distinct you get six solutions for the price of one! $\endgroup$ Dec 6, 2010 at 0:16

5 Answers 5


Let $\displaystyle x,y,z$ be roots of $\displaystyle t^3 + pt^2 + qt + r = 0$.


$\displaystyle x^3 + px^2 + qx + r = 0$      ----- (1)

$\displaystyle y^3 + py^2 + qy + r = 0$    ----- (2)

$\displaystyle z^3 + pz^2 + qz + r = 0$    ----- (3)

Multiply the (1) by $\displaystyle a$, (2) by $\displaystyle b$ and (3) by $\displaystyle c$ and adding gives

$\displaystyle m_3 + p m_2 + q m_1 + rm_0 = 0$    ----- (4)

Multiply the (1) by $\displaystyle ax$, (2) by $\displaystyle by$ and (3) by $\displaystyle cz$ and adding gives

$\displaystyle m_4 + pm_3 + qm_2 + rm_1 = 0$    ----- (5)

Multiply the (1) by $\displaystyle ax^2$, (2) by $\displaystyle by^2$ and (3) by $\displaystyle cz^2$ and adding gives

$\displaystyle m_5 + pm_4 + qm_3 + rm_2 = 0$    ----- (6)

Now (4), (5), (6) is a set of 3 linear equations in 3 variables ($\displaystyle p,q,r$) and can be solved easily.

This give us the cubic which $\displaystyle x,y,z$ satisfy (because we can find out that $\displaystyle p,q,r$ are) which can be solved using Cardano's Method, or more simply by the Trigonometric and Hyperbolic Method.

Once you know $\displaystyle x,y,z$ you can solve for $\displaystyle a,b,c$, as those become just linear equations.

  • $\begingroup$ this is a very elegant solution $\endgroup$ Dec 6, 2010 at 7:38
  • $\begingroup$ Wow! What a clever solution. I like it a lot. Thanks. $\endgroup$
    – echoone
    Dec 7, 2010 at 4:14
  • $\begingroup$ Here is a reference for Moron's method (though it was the quadratic case that was treated there). The matrix corresponding to the linear system Moron came up with is a Hankel matrix, a matrix with constant diagonals. There are $O(n^2)$ methods for solving systems like these, but they tend to be unstable. $\endgroup$ Apr 20, 2011 at 17:04
  • $\begingroup$ @@J.M: Thanks for the link. $\endgroup$
    – Aryabhata
    Apr 20, 2011 at 17:36
  • $\begingroup$ I have to amend my previous comment: it should be "constant antidiagonals", not "constant diagonals". $\endgroup$ Apr 25, 2011 at 9:12

It seems that I am a wee bit late to this particular party, but I thought I'd show yet another way to resolve the OP's algebraic system, which I'll recast here in different notation:

$$\begin{aligned} w_1+w_2+w_3&=m_0\\ w_1 x_1+w_2 x_2+w_3 x_3&=m_1\\ w_1 x_1^2+w_2 x_2^2+w_3 x_3^2&=m_2\\ w_1 x_1^3+w_2 x_2^3+w_3 x_3^3&=m_3\\ w_1 x_1^4+w_2 x_2^4+w_3 x_3^4&=m_4\\ w_1 x_1^5+w_2 x_2^5+w_3 x_3^5&=m_5 \end{aligned}$$

and the problem is to find the $x_i$ and the $w_i$ satisfying the system of equations.

The key here is to recognize that this is exactly the problem of recovering the nodes $x_i$ and weights $w_i$ of an $n$-point Gaussian quadrature rule with some weight function $w(u)$ and some support interval $[a,b]$, given the moments $m_j=\int_a^b w(u)u^j \mathrm du,\quad j=0\dots2n-1$. Recall that $n$-point rules are designed to exactly integrate functions of the form $w(u)p(u)$, where $p(u)$ is a polynomial of degree at most $2n-1$, and this is the reason why we have $2n$ equations.

As is well known, the nodes and the weights of a Gaussian quadrature can be obtained if we know the orthogonal polynomials $P_k(u)$ associated with the weight function $w(u)$. This is due to the fact that the nodes of an $n$-point Gaussian rule are the roots of the orthogonal polynomial $P_n(u)$. The first phase of the problem, now, is determining the set of orthogonal polynomials from the moments.

Luckily, in 1859(!), Chebyshev obtained a method for determining the recursion coefficients $a_k$, $b_k$ for the orthogonal polynomial recurrence

$$P_{k+1}(u)=(u-a_k)P_k(u)-b_k P_{k-1}(u)\quad P_{-1}(u)=0,P_0(u)=1$$

when given the $m_j$. Chebyshev's algorithm goes as follows: initialize the quantities

$$\sigma_{-1,l}=0,\quad l=1,\dots,2n-2$$ $$\sigma_{0,l}=m_l,\quad l=0,\dots,2n-1$$ $$a_0=\frac{m_1}{m_0}$$ $$b_0=m_0$$

and then perform the recursion


for $l=k,\dots,2n-k+1$ and $k=1,\dots,n-1$, from which the recursion coefficients for $k=1,\dots,n-1$ are given by

$$a_k=\frac{\sigma_{k,k+1}}{\sigma_{k,k}}-\frac{\sigma_{k-1,k}}{\sigma_{k-1,k-1}}$$ $$b_k=\frac{\sigma_{k,k}}{\sigma_{k-1,k-1}}$$

I'll skip the details of how the algorithm was obtained, and will instead tell you to look at this paper by Walter Gautschi where he discusses these things.

Once the $a_k$ and $b_k$ have been obtained, solving the original set of equations can be done through the Golub-Welsch algorithm; essentially, one solves a symmetric tridiagonal eigenproblem where the $a_k$ are diagonal entries and the $b_k$ are the off-diagonal entries (the characteristic polynomial of this symmetric tridiagonal matrix is $P_n(x)$). The $x_i$ are the eigenvalues of this matrix, and the $w_i$ can be obtained from the first components of the normalized eigenvectors by multiplying the squares of those quantities with $m_0$.

I have been wholly theoretical up to this point, and you and most other people would rather have code to play with. I thus offer up the following Mathematica implementation of the theory discussed earlier:

(* Chebyshev's algorithm *)

chebAlgo[mom_?VectorQ, prec_: MachinePrecision] := 
 Module[{n = Quotient[Length[mom], 2], si = mom, ak, bk, np, sp, s, v},
  np = Precision[mom]; If[np === Infinity, np = prec];
  ak[1] = mom[[2]]/First[mom]; bk[1] = First[mom];
  sp = PadRight[{First[mom]}, 2 n - 1];
   sp[[k - 1]] = si[[k - 1]];
    v = sp[[j]];
    sp[[j]] = s = si[[j]];
    si[[j]] = si[[j + 1]] - ak[k - 1] s - bk[k - 1] v;
    , {j, k, 2 n - k + 1}];
   ak[k] = si[[k + 1]]/si[[k]] - sp[[k]]/sp[[k - 1]];
   bk[k] = si[[k]]/sp[[k - 1]];
   , {k, 2, n}];
  N[{Table[ak[k], {k, n}], Table[bk[k], {k, n}]}, np]

(* Golub-Welsch algorithm *)

golubWelsch[d_?VectorQ, e_?VectorQ] := 
  MapAt[(First[e] Map[First, #]^2) &, 
    SparseArray[{Band[{1, 1}] -> d, Band[{1, 2}] -> Sqrt[Rest[e]], 
      Band[{2, 1}] -> Sqrt[Rest[e]]}, {Length[d], Length[d]}]], {2}]]

(I note that the implementation here of Chebyshev's algorithm was optimized to use two vectors instead of a two-dimensional array.)

Let's try an example. Let $m_j=j!$ and take the system given earlier ($n=3$):

{d, e} = chebAlgo[Range[0, 5]!]
{{1., 3., 5.}, {1., 1., 4.}}

xw = golubWelsch[d, e]
{{6.2899450829374794, 0.010389256501586145}, {2.2942803602790467, 0.27851773356923976}, {0.4157745567834814, 0.7110930099291743}}

We have here the equivalence xw[[i, 1]]$=x_i$ and xw[[i, 2]]$=w_i$; let's see if the original set of equations are satisfied:

Chop[Table[Sum[xw[[j, 2]] xw[[j, 1]]^i, {j, 3}] - i!, {i, 0, 5}]]
{0, 0, 0, 0, 0, 0}

and they are.

(This example corresponds to generating the three-point Gauss-Laguerre rule.)

As a final aside, the solution given by Aryabhata is an acceptable way of generating Gaussian quadrature rules from moments, though it will require $O(n^3)$ effort in solving the linear equations, as opposed to the $O(n^2)$ effort required for the combination of Chebyshev and Golub-Welsch. Hildebrand gives a discussion of this approach in his book.

Here is Aryabhata's proposal in Mathematica code (after having done the elimination of the appropriate variables in the background):

gaussPolyGen[mom_?VectorQ, t_] := 
 Module[{n = Quotient[Length[mom], 2]}, 
  Expand[Fold[(#1 t + #2) &, 1, Reverse[LinearSolve[
   Apply[HankelMatrix, Partition[mom, n, n-1]], -Take[mom, -n]]]]]]

and compare, using the earlier example:

gaussPolyGen[Range[0, 5]!, t]
-6 + 18*t - 9*t^2 + t^3

% == -6 LaguerreL[3, t] // Simplify

Having found the roots of the polynomial generated by gaussPolyGen[], one merely solves an appropriate Vandermonde linear system to get the weights.

nodes = t /. NSolve[gaussPolyGen[Range[0, 5]!, t], t, 20]
{0.4157745567834791, 2.294280360279042, 6.2899450829374794}

weights = LinearAlgebra`VandermondeSolve[nodes, Range[0, 2]!]
{0.711093009929173, 0.27851773356924087, 0.010389256501586128}

The results here and from the previous method are comparable.


Look at the first three equations as a system of linear equations determining $a$, $b$ and $c$ in terms of $x$, $y$, $z$, $m_0$, $m_1$, $m_2$; the matrix of coefficients is a van der Monde matrix for the triple $x$, $y$, $z$, so it not hard to use Cramer's rule to solve it when no two of them are equal.

Now replace the values you got for $a$, $b$ and $c$ in the second group of three equations. Magically, after simplifying and canceling things common to numerators and denominators, it is a system of polynomial equations in $x$, $y$ and $z$. Moreover, the three left hand sides are symmetric polynomials in $x$, $y$ and $z$, so after expressing those left hand sides in terms of the elementary symmetric polynomials on $x$, $y$ and $z$, you end up with a not too difficult polynomial system.

Solve it.

Now you have the values of $x+y+z$, $xy+xz+yz$ and $xyz$. Solving a cubic equation now gives you $x$, $y$ and $z$ and, from the first part, also $a$, $b$ and $c$.

NB: Following these steps is easily doable in Mathematica and you get an answer. The answer is simply too huge, and I doubt it can be massaged into something sensible---and I hence doubt there is a sensible way to solve the system of equations by hand.

Mma, on the other hand, did not solve the system as it is originally given in the time I gave it.

NB2: For comparison, the system $$\left\{\begin{aligned}a + b = m_0\\ax+by=m_1\\ax^2+by^2=m_2\\ax^3+by^3=m_3\end{aligned}\right.$$ has as solution, according to Mma (and this one it does compute it by itself): $$\begin{aligned} &a= \frac{-2 m_1^3+3 m_0 m_2 m_1+m_0 \left(\sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+4 m_0 m_2^3+m_0^2 m_3^2}-m_0 m_3\right)}{2 \sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+m_0 \left(4 m_2^3+m_0 m_3^2\right)}} \\ &b = \frac{2 m_1^3-3 m_0 m_2 m_1+m_0 \left(m_0 m_3+\sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+4 m_0 m_2^3+m_0^2 m_3^2}\right)}{2 \sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+m_0 \left(4 m_2^3+m_0 m_3^2\right)}} \\ &x = \frac{-m_1 m_2+m_0 m_3+\sqrt{\left(m_1 m_2-m_0 m_3\right){}^2+4 \left(m_1^2-m_0 m_2\right) \left(m_1 m_3-m_2^2\right)}}{2 \left(m_0 m_2-m_1^2\right)} \\ &y = \frac{m_1 m_2-m_0 m_3+\sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+4 m_0 m_2^3+m_0^2 m_3^2}}{2 m_1^2-2 m_0 m_2} \end{aligned} $$ and $$\begin{aligned} &a=\frac{2 m_1^3-3 m_0 m_2 m_1+m_0 \left(m_0 m_3+\sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+4 m_0 m_2^3+m_0^2 m_3^2}\right)}{2 \sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+m_0 \left(4 m_2^3+m_0 m_3^2\right)}} \\ &b=\frac{-2 m_1^3+3 m_0 m_2 m_1+m_0 \left(\sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+4 m_0 m_2^3+m_0^2 m_3^2}-m_0 m_3\right)}{2 \sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+m_0 \left(4 m_2^3+m_0 m_3^2\right)}} \\ &x=-\frac{m_1 m_2-m_0 m_3+\sqrt{\left(m_1 m_2-m_0 m_3\right){}^2+4 \left(m_1^2-m_0 m_2\right) \left(m_1 m_3-m_2^2\right)}}{2 \left(m_0 m_2-m_1^2\right)} \\ &y=-\frac{-m_1 m_2+m_0 m_3+\sqrt{4 m_3 m_1^3-3 m_2^2 m_1^2-6 m_0 m_2 m_3 m_1+4 m_0 m_2^3+m_0^2 m_3^2}}{2 \left(m_1^2-m_0 m_2\right)} \end{aligned} $$


@Aryabhata's solution in insightful, but I'll present the (tedious, but thoughtlessly mechanical) methodology I tend to use to approach such problems: resultants.

The resultant of two polynomials is a polynomial with one variable eliminated. Therefore, you can proceed to solve a polynomial system in roughly the same way you would with a linear system (in theory).

First, convert your equations to polynomials, $p_i$, so that the roots of these polynomials are the solutions to your equations.

$$\begin{eqnarray} p_0 &=& a+b+c-m_0\\ p_1 &=& a x + b y + c z - m_1\\ p_2 &=& a x^2 + b y^2 + c z^2 - m_2\\ p_3 &=& a x^3 + b y^3 + c z^3 - m_3\\ p_4 &=& a x^4 + b y^4 + c z^4 - m_4\\ p_5 &=& a x^5 + b y^5 + c z^5 - m_5 \end{eqnarray}$$

Pick your favorite one (say, $p_0$), and use it to compute a bunch of resultants (using Mathematica's ${\tt Resultant[]}$ function) that eliminate one variable (say, $a$):

$$\begin{eqnarray} r_0 &=& {\tt Resultant[} p_0, p_1, a {\tt ]} = -m_1 - b x - c x + m_0 x + b y + c z\\ r_1 &=& {\tt Resultant[} p_0, p_2, a {\tt ]} = -m_2 - b x^2 - c x^2 + m_0 x^2 + b y^2 + c z^2 \\ r_2 &=& {\tt Resultant[} p_0, p_3, a {\tt ]} = -m_3 - b x^3 - c x^3 + m_0 x^3 + b y^3 + c z^3\\ r_3 &=& {\tt Resultant[} p_0, p_4, a {\tt ]} = -m_4 - b x^4 - c x^4 + m_0 x^4 + b y^4 + c z^4\\ r_4 &=& {\tt Resultant[} p_0, p_5, a {\tt ]} = -m_5 - b x^5 - c x^5 + m_0 x^5 + b y^5 + c z^5\\ \end{eqnarray}$$

(Of course, the above amounts to simply solving $p_0 = 0$ for $a$ and substituting into the other $p_i$, since the system is linear in $a$. The same will be true when eliminating $b$ and $c$, so using resultants here is a little bit of overkill, but the process is mechanical enough to be about as easy to (not-)think about as, say, Gaussian elimination.)

Now, we use, say, $r_0$ to compute resultants that eliminate $b$, and so forth. For your special system, the polynomials at each step happen to have common factors that give rise to possible solutions; you might need to consider those separately, but they tend to represent "special case" scenarios. In the following, I base the next resultant step on the uncommon factors (indicated with upper-case variables).

$$\begin{eqnarray} s_i &=& {\tt Resultant[} r_0, r_i, b {\tt ]} = S_i \cdot (x-y), \hspace{0.2in}i = 1, 2, 3, 4\\ t_i &=& {\tt Resultant[} S_0, S_i, c {\tt ]} = T_i \cdot (y-z)(z-x), \hspace{0.2in}i = 1, 2, 3 \\ u_i &=& {\tt Resultant[} T_0, T_i, x {\tt ]} = U_i \cdot (m_2-m_1 y - m_1 z + m_0 y z ), \hspace{0.2in}i = 1, 2 \\ v_1 &=& {\tt Resultant[} U_0, U_1, y {\tt ]} \end{eqnarray}$$

The final polynomial, $v_1$, has just one variable, $z$.

$$\begin{eqnarray}v_1 &=& \left(m_2^2 - m_1 m_3 - m_1 m_2 z + m_0 m_3 z + m_1^2 z^2 - m_0 m_2 z^2\right)^4 \\ && \left(-m_3^3 + 2 m_2 m_3 m_4 - m_1 m_4^2 - m_2^2 m_5 + m_1 m_3 m_5 \right.\\ &&\left. + z\left(m_2 m_3^2 - m_2^2 m_4 - m_1 m_3 m_4 + m_0 m_4^2 + m_1 m_2 m_5 - m_0 m_3 m_5 \right) \right. \\ &&\left. +z^2\left(- m_2^2 m_3 + m_1 m_3^2 + m_1 m_2 m_4 - m_0 m_3 m_4 - m_1^2 m_5 + m_0 m_2 m_5\right) \right. \\ &&\left. + z^3\left( m_2^3 - 2 m_1 m_2 m_3 + m_0 m_3^2 + m_1^2 m_4 - m_0 m_2 m_4\right)\right)^2\end{eqnarray}$$

The first factor is another "special case", so that $z$, in general, should be a root of the cubic polynomial in the second factor. To get $y$, and $x$, and the rest, you could back-substitute into polynomials from earlier in the resultant chain (just as you would with a linear system), or you could just compute resultant chains that eliminate every variable except $y$, then every variable except $x$, etc.

It turns out that (as one might expect) your particular system is such that the "final" polynomials for $x$, $y$, and $z$ are equivalent; that is, $x$, $y$, and $z$ are roots of the cubic in the second factor of $v_1$ above. (This is, in fact, @Aryabhata's cubic.)

I'll note that resultants in general are computationally expensive (especially in symbolic form). Very often, the process can bog down completely in Mathematica, even for fairly modest systems, and/or the final polynomial can be of enormous degree. Sometimes, a bit of finesse in the order of elimination helps. My first attempt at finding (a chain of resultants that eliminated everything but) $a$ got boggy, but eliminating in the order $b$, $c$, $z$, $y$, $x$ gave me a cubic equation (and some "special case" factors) ... which is huge, so I won't present it here. A more-practical approach here might be to find $x$, $y$, and $z$ from the cubic polynomial, substitute these values back into three of the $p_i$, and solve the linear system for $a$, $b$, and $c$.


HINT $\ $ Exploit the innate symmetry of the problem - here power sums as symmetric functions.

The power sum $\rm\ \ \Sigma_n = a\ x^n + b\ y^n + c\ z^n\ \ $ satisfies the recursion

$$\rm \Sigma_{n+3} - (x+y+z)\ \Sigma_{n+2} + (xy+yz+zx)\ \Sigma_{n+1} - xyz\ \Sigma_n\ =\ 0 $$

For $\rm\ n = 0,1,2\ $ the above yields a linear system for $\rm\: \ x+y+z,\ \ xy+yz+zx,\ \ xyz\ ,\:$ which are the coefficients of the cubic whose roots are $\rm\ x, y, z$

REMARK $\ $ As I mentioned in an answer to a similar question, readers familiar with the theory of Lucas-Lehmer sequences may recognize this recursion as one of many useful identities satisfied by sums of powers. Such identities arise in many diverse contexts. Here is an interesting example: Capdegelle's work on FLT (Fermat's Last Theorem) employed the power sum recursion $\rm\ F_{n+3} + S_2\: F_{n+2} + S_1\: F_{n+1} + S_0\: F_n = 0\ $ where $\rm\ F_n = X^n + Y^n - Z^n\ $ is the Fermat polynomial and the polynomials $\rm\ S_k\ $ are the elementary symmetric polynomials $\rm\ S_0 = -XYZ,$ $\rm\ S_1 = XY + XZ + YZ,\ $ $\rm S_2 = -(X+Y+Z)\:.\ $


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