# Is it possible to train a neural network to solve polynomial equations?

• I randomly generate millions groups of triplet $\lbrace x_0, x_1, x_2 \rbrace$ within range $(0,1)$, then calculate the corresponding coefficients of the polynomial $(x-x_0)(x-x_1)(x-x_2)$, which result in triplet groups normalized in a form of $\lbrace { {x_0+x_1+x_2 \over 3} , {\sqrt{x_0x_1+x_1x_2+x_0x_2 \over 3}} , {\sqrt{x_0x_1x_2}}} \rbrace$;

• After that, I feed the coefficient triplets in to a 5-layered neural network $\lbrace 3,4,5,4,3 \rbrace$, in which all the activation function is set to sigmoid and the learning rate is set to 0.1;

• However, I only get a very poor cross validation, around 20%.

How can I fix this?

BackGround

My original problem is a dynamic inverse problem. In that problem, I have hundreds of thousands of observations $O$, from these observations, I need to recover several hundred parameters $P$. The simulation process from $P$ to $O$ is very easy and cheap to calculate, but the inversion from $O$ to $P$ is highly nonlinear and nearly impossible. My idea is to train a neural network getting $O$ as inputs and $P$ as outputs. To check the feasibility of this idea, I employ a 3-ordered polynomial equation to do the validation.

update half a year later

With more nodes per layer, I have successfully trained a neural network. The topology is set to $\lbrace 3, 64, 64, 64 \rbrace$. And the most important trick is, sorting the generated triplet $\lbrace x_0, x_1, x_2 \rbrace$, ensuring $x_0 <= x_1 <= x_2$ always holds.

• I would create a neural network to estimate the number of real roots then something like Newton's method to estimate them. – Emre Feb 9 '17 at 18:42
• @Emre In the numeric experiment above, both the training and the validation sets, I always have three real roots in range (0,1). So I do not need to estimate the number of real roots in this case. – Feng Wang Feb 9 '17 at 19:49
• I don't get the role of the polynomial by now. Could you please expand on this? Can't you collect many pairs (P, O) and take a subset of those for validation? – Martin Thoma Feb 9 '17 at 20:58
• @MartinThoma I want to test my idea with a much simpler inversion problem. I mean, the coefficients triplet as observation $O$, and the roots triplet as parameter $P$ to recover, where the process from $\{x_0, x_1, x_2\}$ to polynomial coefficients is straightforward, but the inversion is much difficult. If I can successfully train such a neural network taking coefficients as inputs and roots as outputs, I would like to migrate this idea to my dynamic inverse problem in the next step. – Feng Wang Feb 9 '17 at 21:11
• @MartinThoma I can always generate as many as possible pairs of $\{P, O\}$ to train and to validate the performance. – Feng Wang Feb 9 '17 at 21:18

In particular, the function you are trying to compute amounts to computing $x_0,x_1,x_2$ from $a,b,c,d$, where
\begin{align*} x_k &= - {1 \over 3a} \left(b + \eta^k C + {\Delta_0 \over \eta^k C}\right)\\ \Delta_0 &= b^2 - 3ac\\ C &= \sqrt{\Delta_1 \pm \sqrt{\Delta_1^2 - 4\Delta_0^3} \over 2}\\ \Delta_1 &= 2b^3 - 9abc + 27a^2d\\ \eta &= -{1 \over 2} + {1 \over 2} \sqrt{3} i \end{align*}