A Parameterization of Newton Polytope of a Polynomial with All Positive Coefficients

Consider a polynomial p=∑IaIxIp=\sum_{I} a_I x^I with aI≥0a_I\geq 0 for all II , where xI=∏i=1NxiniI, x^I=\prod_{i=1}^N x_i^{n^I_i}, its Newton polytope N(p)N(p) is defined as the convex hull of vectors {nI=(n1I,…,nNI)}\{\mathbf{n}^I=(n^I_1,\dots,n^I_N)\} .

Now consider the `scattering map’ X(x)=∂log⁡p∂log⁡x, \mathbf X(x)=\frac{\partial \log p}{\partial \log \mathbf x}, where xi∈(0,∞)x_i\in (0,\infty) for all ii . We claim that X\mathbf{X} is an one-to-one map between (0,∞)N(0,\infty)^N and the interior of N(p)N(p) .

Firstly, suppose the matrix (niI)(n_i^I) does not have full rank, i.e. there exists s≠0\mathbf s\neq 0 such that s⋅nI=0\mathbf s \cdot \mathbf n^I=0 for all II . In this case, s⋅X=s⋅∂log⁡p∂log⁡x=0. \mathbf s\cdot\mathbf X=\mathbf s\cdot\frac{\partial \log p}{\partial \log \mathbf x}=0. Therefore, one can project im⁡(X)\operatorname{im}(\mathbf X) by setting some xi=1x_i=1 so that there’s no more vector s\mathbf s such that s⋅nI=0\mathbf s\cdot \mathbf n^I=0 for all II . Conversely, one can recover the original image by solving these equations s⋅X=0\mathbf s\cdot\mathbf X=0 . Therefore, we can assume that the matrix (niI)(n_i^I) has full rank, then dim⁡(N(p))=N\dim (N(p))=N .

Next we show that interior points of N(p)N(p) are in the im⁡(X)\operatorname{im}(\mathbf{X}) . Let Λ\mathbf{\Lambda} be a interior point in N(p)N(p) defined by Λ=∑IλInI=∂∂log⁡x∑IλIlog⁡xI \mathbf \Lambda=\sum_{I}\lambda_I\mathbf n^I =\frac{\partial}{\partial \log \mathbf x}\sum_{I}\lambda_I \log x^I where ∑IλI=1\sum_I \lambda_I=1 and λI>0\lambda_I > 0 . Equations X(x)=Λ\mathbf X(x)=\mathbf \Lambda are 0=∂∂log⁡x(log⁡p−∑IλIlog⁡xI)=∂∂log⁡x(log⁡F(x))=1F(x)∂F(x)∂log⁡x \begin{aligned} 0=\frac{\partial }{\partial \log \mathbf x}\left( \log p-\sum_{I}\lambda_I \log x^I \right)=\frac{\partial }{\partial \log \mathbf x}\left( \log F(\mathbf x) \right)=\frac{1}{F(\mathbf x)}\frac{\partial F(\mathbf x)}{\partial \log \mathbf x} \end{aligned} where F(x)=∑IaIxI∏J(x−J)λJ. F(\mathbf x)=\sum_I a_I x^I\prod_J (x^{-J})^{\lambda_J}. Let y=log⁡x\mathbf y=\log \mathbf x , and then F(y)=∑IaIexp⁡(y⋅(nI−Λ)) \begin{aligned} F(\mathbf y) &=\sum_I a_I \exp\left(\mathbf{y}\cdot \left(\mathbf{n}^I-\mathbf{\Lambda}\right)\right) \end{aligned} so we only need to show that F(y)F(\mathbf y) has saddle points in RN\mathbb R^N because F>0F>0 for all y\mathbf y .

To see it, we firstly notice that F(y)F(\mathbf y) is a strict convex function of y\mathbf y . In fact, the Hessian of F(y)F(\mathbf y) is Hij(y)=∂2∂yi∂yjF(y)=∑IaIexp⁡(y⋅(nI−Λ))(nI−Λ)i(nI−Λ)j. H_{ij}(\mathbf y)=\frac{\partial^2}{\partial y_i\partial y_j}F(\mathbf y)=\sum_I a_I \exp\left(\mathbf{y}\cdot \left(\mathbf{n}^I-\mathbf{\Lambda}\right)\right)\left(\mathbf{n}^I-\mathbf{\Lambda}\right)_i\left(\mathbf{n}^I-\mathbf{\Lambda}\right)_j. For any v≠0\mathbf v\neq 0 , ∑i,jvivjHij=∑IaIexp⁡(y⋅(nI−Λ))(v⋅(nI−Λ))2>0. \sum_{i,j}v_iv_jH_{ij}=\sum_I a_I \exp\left(\mathbf{y}\cdot \left(\mathbf{n}^I-\mathbf{\Lambda}\right)\right) \left(\mathbf v\cdot (\mathbf{n}^I-\mathbf{\Lambda})\right)^2 >0. It cannot vanish because that it only happens when v⋅(nI−Λ)=0\mathbf v\cdot (\mathbf{n}^I-\mathbf{\Lambda})=0 for all II , but we have assumed {nI}\{\mathbf n^I\} has full rank.

For a strict convex function F(y)F(\mathbf y) on RN\mathbb R^N , it has a unique minimal in RN\mathbb R^N iff it does not take its minimal when y\mathbf{y} goes to infinity along any direction. In our case, we only need that for any y≠0\mathbf{y}\neq 0 and Λ∈(N(p))∘\mathbf\Lambda \in (N(p))^\circ , there exist some II such that y⋅(nI−Λ)>0 \mathbf{y}\cdot (\mathbf{n}^I-\mathbf{\Lambda})>0 and if Λ∉N(p)\mathbf\Lambda \not\in N(p) , there exist some y≠0\mathbf{y}\neq 0 such that the exponentials are all negative, then F(ty)→0F(t\mathbf y)\to 0 when t→∞t\to \infty .

In fact, if Λ∈(N(p))∘\mathbf\Lambda \in (N(p))^\circ , for a given y≠0\mathbf y\neq 0 , the hyperplain LL with normal vector y\mathbf y crossing Λ\mathbf \Lambda divides the space RN\mathbb R^N into semi-spaces L+L^+ and L−L^- , then nI\mathbf{n}^I in L+L^+ are what we are looking for. Conversely, if Λ∉N(p)\mathbf\Lambda \not\in N(p) , one can find a hyperplain LL such that N(p)⊂L−N(p)\subset L^- , then the normal vector of LL gives the wanted direction. These are direct results of convexity of N(p)N(p) .

Therefore, we have proven that F(y)F(\mathbf y) has a unique minimal in RN\mathbb R^N when Λ∈(N(p))∘\mathbf \Lambda \in (N(p))^\circ and no minimal in RN\mathbb R^N when Λ∉N(p)\mathbf \Lambda \not\in N(p) . Hence, X(x)=∂log⁡p∂log⁡x, \mathbf X(x)=\frac{\partial \log p}{\partial \log \mathbf x}, is an one-to-one map between (0,∞)N(0,\infty)^N and the interior of N(p)N(p) .

Generally, let Λ\mathbf{\Lambda} be a interior point in Minkowski sum ∑pαpN(p)\sum_p \alpha_p N(p) corresponding to P=∏ppαpP=\prod_p p^{\alpha_p} defined by Λ=∑pαpΛp=∑pαp∑IpλIpnIp=∂∂log⁡x∑pαp∑IpλIplog⁡xIp \mathbf{\Lambda} =\sum_p \alpha_p \mathbf{\Lambda}_p =\sum_p \alpha_p \sum_{I_p}\lambda_{I_p}\mathbf{n}^{I_p} =\frac{\partial}{\partial \log \mathbf{x}}\sum_{p}\alpha_p\sum_{I_p}\lambda_{I_p} \log x^{I_p} where ∑IpλIp=1\sum_{I_p} \lambda_{I_p}=1 and λIp>0\lambda_{I_p} > 0 . Equations X(x)=Λ\mathbf{X}(x)=\mathbf{\Lambda} are 0=∂∂log⁡x(log⁡P−∑pαp∑IpλIplog⁡xIp)=∂∂log⁡x(log⁡(∏pFpαp)), \begin{aligned} 0=\frac{\partial }{\partial \log \mathbf{x}}\left( \log P-\sum_{p}\alpha_p\sum_{I_p}\lambda_{I_p} \log x^{I_p} \right)&=\frac{\partial }{\partial \log \mathbf{x}}\left( \log \left(\prod_p F_p^{\alpha_p}\right) \right), \end{aligned} where Fp(y)=∑IpaIpexp⁡(y⋅(nIp−Λp)). \begin{aligned} F_p(\mathbf y)&=\sum_{I_p} a_{I_p} \exp\left(\mathbf{y}\cdot \left(\mathbf{n}^{I_p}-\mathbf{\Lambda}_p\right)\right). \end{aligned} Write F(y)=∏pFp(y)αp, F(\mathbf y)=\prod_p F_p(y)^{\alpha_p}, we only need to show that F(y)F(y) has a unique saddle point in RN\mathbb R^N .

We assume that αp>0\alpha_p>0 for all pp . Here, F(y)F(\mathbf y) is no longer a strict convex function. However, take a positive number α0\alpha_0 such that α0<αp\alpha_0< \alpha_p for all pp , consider a new function F(y)1/α0=∏pFp(y)αp/α0, F(y)^{1/\alpha_0}=\prod_p F_p(y)^{\alpha_p/\alpha_0}, since FpF_p are convex functions and αp/α0>1\alpha_p/\alpha_0>1 , Fp(y)αp/α0F_p(y)^{\alpha_p/\alpha_0} are convex functions, so is their product. Since F(y)1/α0F(y)^{1/\alpha_0} has the same minimums as F(y)F(y) , so we can further assume that αp>1\alpha_p>1 for all pp .

Finally, we claim that F(y)F(y) does not take minimum when y\mathbf{y} goes to infinity along any direction. It is because that for a given y\mathbf{y} and each pp , Fp(ty)→∞F_p(t\mathbf{y})\to \infty when t→∞t\to \infty , so does F(ty)=∏pFp(ty)αpF(t\mathbf{y})=\prod_p F_p(t\mathbf{y})^{\alpha_p} . Therefore, equation X(x)=Λ\mathbf{X}(x)=\mathbf{\Lambda} has a unique solution.

Buwai Lee

Buwai Lee

交换图都不会画的魔法师