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A spectral partitioning algorithm for maximum directed cut problem

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Abstract

We investigate the maximum directed cut (MaxDC) problem by designing a spectral partitioning algorithm. Given a directed graph with nonnegative arc weights, we wish to obtain a bipartition of the vertices such that the total weight of the directed cut arcs is maximized. Relaxing the MaxDC problem as a quadratic program allows us to explore combinatorial properties of the optimal solution, leading to a 0.272-approximation algorithm via the technique of spectral partitioning rounding.

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Acknowledgements

The first author is supported by Beijing Excellent Talents Funding (No. 201400 0020124G046), and General Science and Technology Project of Beijing Municipal Education Commission (No. KM201810005006). The second author’s research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Grant 06446, and NSFC (Nos. 11771386 and 11728104). The third author’s research is supported by NSFC (No. 11501412). The fourth author’s research is supported by NSFC (Nos. 11531014 and 11871081). The fifth author is supported by Higher Educational Science and Technology Program of Shandong Province (No. J15LN22).

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Correspondence to Dongmei Zhang.

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A preliminary version of this paper appeared in Proceedings of the 11th International Conference on Combinatorial Optimization and Applications, pp 298–312, 2017.

Appendix

Appendix

We give the proof of Lemma 2. The probability of a cut arc is given by

$$\begin{aligned} \begin{aligned} C(i,j)=&\mathbb {P}\left\{ \left( \begin{array}{c} y_{i} \\ y_{j} \\ \end{array} \right) \in \left\{ \left( \begin{array}{c} \mathrm {sgn}(x_{0}) \\ -\mathrm {sgn}(x_{0}) \\ \end{array} \right) ,\left( \begin{array}{c} -\mathrm {sgn}(x_{0}) \\ \mathrm {sgn}(x_{0}) \\ \end{array} \right) \right\} \right\} \\ =&\mathbb {P}\left\{ x_{0}x_{i}>\sqrt{t},x_{0}x_{j}<-\sqrt{t}~\mathrm {or}~ x_{0}x_{i}<-\sqrt{t},x_{0}x_{j}>\sqrt{t}\right\} \\ =&\mathbb {P}\left\{ \sqrt{t}<x_{0}x_{i},\sqrt{t}<-x_{0}x_{j}~\mathrm {or}~ \sqrt{t}<-x_{0}x_{i},\sqrt{t}<x_{0}x_{j}\right\} \\ =&\mathbb {P}\left\{ \sqrt{t}<\min \{x_{0}x_{i},-x_{0}x_{j}\}~\mathrm {or}~ \sqrt{t}<\min \{-x_{0}x_{i},x_{0}x_{j}\}\right\} .\\ \end{aligned} \end{aligned}$$
(23)

Similarly, the probability of a crossing arc can be obtained by

$$\begin{aligned} \begin{aligned} X(i,j)=&\mathbb {P}\left\{ \left( \begin{array}{c} y_{i} \\ y_{j} \\ \end{array} \right) \in \left\{ \left( \begin{array}{c} \mathrm {sgn}(x_{0}) \\ 0 \\ \end{array} \right) ,\left( \begin{array}{c} 0 \\ -\mathrm {sgn}(x_{0}) \\ \end{array} \right) ,\left( \begin{array}{c} -\mathrm {sgn}(x_{0}) \\ 0 \\ \end{array} \right) ,\left( \begin{array}{c} 0 \\ \mathrm {sgn}(x_{0}) \\ \end{array} \right) \right\} \right\} \\ =&\mathbb {P}\Big \{x_{0}x_{i}>\sqrt{t},|x_{0}x_{j}|\le \sqrt{t}~\mathrm {or}~ |x_{0}x_{i}|\le \sqrt{t},x_{0}x_{j}<-\sqrt{t}\\&~\mathrm {or}~x_{0}x_{i}<-\sqrt{t},|x_{0}x_{j}|\le \sqrt{t}~\mathrm {or}~ |x_{0}x_{i}|\le \sqrt{t},x_{0}x_{j}>\sqrt{t}\Big \}\\ =&\mathbb {P}\Big \{|x_{0}x_{j}|\le \sqrt{t}<x_{0}x_{i}~\mathrm {or}~|x_{0} x_{i}|\le \sqrt{t}<-x_{0}x_{j}\\&~\mathrm {or}~|x_{0}x_{j}|\le \sqrt{t}<-x_{0}x_{i}~\mathrm {or}~|x_{0}x_{i} |\le \sqrt{t}<x_{0}x_{j}\Big \}. \end{aligned} \end{aligned}$$
(24)

In the following, we prove equation (10). Note that the vector x is derived by Algorithm 1. We consider three cases.

  1. Case 1.

    If \(x_{i}\) and \(x_{j}\, (i,j\in \{1,2,\ldots ,n\})\) are both equal to zero, (10) holds obviously.

  2. Case 2.

    If exactly one of \(x_{i}\) and \(x_{j}\) is zero, then for symmetry, assume that \(x_{i}\) is zero.

    1. Case 2.1.

      \(x_{j}\ne 0\) and \(x_jx_0\ge 0\). We obtain \(C(i,j)=0\) and \(X(i,j)=x_{0}^{2}x_{j}^2\). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta ) x_{0}^{2}x_{j}^2\\&=\beta (1-\beta ) x_{0}^{2}\left( x_{j}^2-2x_{0}x_{j}\right) +2\beta (1-\beta )x^3_{0}x_{j}\\&\ge \beta (1-\beta ) x_{0}^{2}\left( x_{j}^2-2x_{0}x_{j}\right) -2\beta (1-\beta )x^2_{0}x^2_{j}.\\ \end{aligned} \end{aligned}$$
    2. Case 2.2.

      \(x_{j}\ne 0\) and \(x_jx_0\le 0\). We easily obtain \(C(i,j)=0\) and \(X(i,j)=x_{0}^2x_{j}^2\). Since \(-x_{0}x_{j}\le x_{j}^{2}\), we get

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^{2}x_{j}^2\\&=\beta (1-\beta )x_{0}^{2}(x_{j}^{2}-2x_{0}^{2}x_{j})+2\beta (1-\beta )x_{0}^{3}x_{j}\\&\ge \beta (1-\beta )x_{0}^{2}(x_{j}^{2}-2x_{0}^{2}x_{j})-2\beta (1-\beta )x_{0}^{2}x_{j}^{2}. \end{aligned} \end{aligned}$$

    Equation (10) holds in these cases.

  3. Case 3.

    \(x_{i}x_{j}\ne 0\). According to the signs and the sizes of \(x_{i}\) and \(x_{j}\), we consider eight cases in the following.

    1. Case 3.1.

      \(x_{0}x_{i}> 0\), \(x_{0}x_{j}> 0\), and \(|x_{0}x_{j}| \le |x_{0}x_{i}|\) (\( x_{0}x_{j}\le x_{0}x_{i}\)). Then we have

      $$\begin{aligned} \begin{aligned} C(i,j)=&0,\\ X(i,j)=&\mathbb {P}\left\{ |x_{0}x_{j}|\le \sqrt{t}<x_{0}x_{i}\right\} =x_{0}^2x_{i}^2-x_{0}^2x_{j}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)=\,&\beta \left( x_{0}^2x_{i}^2-x_{0}^2x_{j}^2\right) \\ =\,&\beta \left( x_{0}x_{i}+x_{0}x_{j}\right) \left( x_{0}x_{i}-x_{0}x_{j}\right) \\ \ge \,&\beta (1-\beta )\left( x_{0}x_{i}-x_{0}x_{j}\right) ^{2}\\ =\,&\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\ =\,&\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j} -2x_{i}x_{j}\right) \\ \,&-\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned} \end{aligned}$$

      Since \(x_{0}^2\le x_{0}x_{i}+x_{0}x_{j}\), we get

      $$\begin{aligned} \begin{aligned} 2x_{0}^2\left( x_{0}x_{i}-x_{0}x_{j}\right) \le \,&2\left( x_{0}x_{i} +x_{0}x_{j}\right) \left( x_{0}x_{i}-x_{0}x_{j}\right) \\ =\,&2\left( x_{0}^2x_{i}^2-x_{0}^2x_{j}^2\right) =2X(i,j). \end{aligned} \end{aligned}$$

      Therefore,

      $$\begin{aligned} \begin{aligned} -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge&-\beta (1-\beta )2X(i,j)\\ =&-\beta (1-\beta )2X(i,j)-\beta (1-\beta )4C(i,j). \end{aligned} \end{aligned}$$

      Finally,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    2. Case 3.2.

      \(x_{i}\), \(x_{j}\) and \(x_{0}\) all have the same sign, and \(|x_{0}x_{i}|\le |x_{0}x_{j}|\) (\(x_{0}x_{i}\le x_{0}x_{j}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)&=0,\\ X(i,j)&=\mathbb {P}\left\{ |x_{0}x_{i}|\le \sqrt{t}<x_{0}x_{j}\right\} =x_{0}^2x_{j}^2-x_{0}^2x_{i}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)=\,&\beta \left( x_{0}^2x_{j}^2-x_{0}^2x_{i}^2\right) \\ =\,&\beta \left( x_{0}x_{j}+x_{0}x_{i}\right) \left( x_{0}x_{j}-x_{0}x_{i}\right) \\ \ge \,&\beta (1-\beta )\left( x_{0}x_{j}-x_{0}x_{i}\right) ^{2}\\ =\,&\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\ =\,&\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j} -2x_{i}x_{j}\right) \\ \,&-\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned} \end{aligned}$$

      Since \(x_{0}x_{i}-x_{0}x_{j}\le 0\), we have \(-\beta (1-\beta )x_{0}^2 \left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge 0\). Moreover, \(-\left( x_{0}x_{j}+x_{0}x_{i}\right) \le 0\). Therefore

      $$\begin{aligned} \begin{aligned}&-\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge 0\\&\quad \ge -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \left[ -\left( x_{0} x_{i}+x_{0}x_{j}\right) \right] \\&\quad \ge -\beta (1-\beta )2\left( x_{0}^2x_{j}^2-x_{0}^2x_{i}^2\right) \\&\quad =-\beta (1-\beta )2X(i,j)\\&\quad =-\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$

      Finally,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)\ge&\,\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&-\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    3. Case 3.3

      \(x_{0}x_{i}< 0\), \(x_{0}x_{j}< 0\) and \(|x_{0}x_{j}|\le |x_{0}x_{i}|\) (\(x_{0}x_{i}\le x_{0}x_{j}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)&= 0,\\ X(i,j)&= \mathbb {P}\left\{ |x_{0}x_{j}|\le \sqrt{t}<-x_{0}x_{i}\right\} =x_{0}^2x_{i}^2-x_{0}^2x_{j}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&= \beta \left( x_{0}^2x_{i}^2-x_{0}^2x_{j}^2\right) \\&\ge \beta (1-\beta )\left| x_{0}x_{i}+x_{0}x_{j}\right| \left| x_{0}x_{i} -x_{0}x_{j}\right| \\&\ge \beta (1-\beta )\left( x_{0}x_{i}-x_{0}x_{j}\right) ^{2}\\&= \beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\&= \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j} -2x_{i}x_{j}\right) \\&\quad -\,\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned} \end{aligned}$$

      Because of \(\left| x_{0}x_{i}-x_{0}x_{j}\right| \le \left| x_{0}x_{i} +x_{0}x_{j}\right| \), the third inequality follows from the second one. Since \(\left( x_{0}x_{i}-x_{0}x_{j}\right) \le 0\), we have

      $$\begin{aligned} -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge 0. \end{aligned}$$

      Moreover \(\left( x_{0}x_{i}+x_{0}x_{j}\right) \le 0\), we have

      $$\begin{aligned} \begin{aligned}&-\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge 0\\&\quad \ge -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \left( x_{0} x_{i}+x_{0}x_{j}\right) \\&\quad \ge -\beta (1-\beta )2\left( x_{0}^2x_{i}^2-x_{0}^2x_{j}^2\right) \\&\quad =-\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$

      Therefore,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2 +2x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    4. Case 3.4.

      \(x_{0}x_{i}<0\), \(x_{0}x_{j}<0\), and \(|x_{0}x_{i}|\le |x_{0}x_{j}|\) (\(x_{0}x_{j}\le x_{0}x_{i}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)&=0,\\ X(i,j)&=\mathbb {P}\left\{ |x_{0}x_{i}|\le \sqrt{t}<-x_{0}x_{j}\right\} =x_{0}^2x_{j}^2-x_{0}^2x_{i}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&=\beta \left( x_{0}^2x_{j}^2-x_{0}^2x_{i}^2\right) \\&\ge \beta (1-\beta )\left| x_{0}x_{i}+x_{0}x_{j}\right| \left| x_{0}x_{i} -x_{0}x_{j}\right| \\&\ge \beta (1-\beta )\left( x_{0}x_{j}-x_{0}x_{i}\right) ^{2}\\&=\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\&=\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j} -2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned} \end{aligned}$$

      Because of \(\left| x_{0}x_{i}-x_{0}x_{j}\right| \le \left| x_{0}x_{i} +x_{0}x_{j}\right| \), the third inequality follows from the second one. Meanwhile, from \(x_{0}^2\le -\left( x_{0}x_{i}+x_{0}x_{j}\right) \) and

      $$\begin{aligned} \begin{aligned} x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right)&\le -\left( x_{0}x_{i} +x_{0}x_{j}\right) \left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \\&=2\left( x_{0}^2x_{j}^2-x_{0}^2x_{i}^2\right) \\&=2X(i,j), \end{aligned} \end{aligned}$$

      we have

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    5. Case 3.5.

      \(x_{0}x_{i}> 0\), \(x_{0}x_{j}<0\), and \(|x_{0}x_{j}|\le |x_{0}x_{i}|\) (\(-x_{0}x_{j}\le x_{0}x_{i}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)=&x_{0}^2x_{j}^2,\\ X(i,j)=&\mathbb {P}\left\{ |x_{0}x_{j}|\le \sqrt{t}<x_{0}x_{i}\right\} =x_{0}^2x_{i}^2-x_{0}^2x_{j}^2. \end{aligned} \end{aligned}$$

      The inequality (Beckenbach and Bellman 1961)

      $$\begin{aligned} (1-\beta )a^2+\beta b^2\ge \beta (1-\beta )(a+b)^{2} \end{aligned}$$

      holds for \(a,b\ge 0\), and \(0\le \beta \le 1\). Since \(x_{0}x_{i}>0\), \(-x_{0}x_{j}>0\), we obtain that

      $$\begin{aligned} C(i,j)+\beta X(i,j)&=x_{0}^2x_{j}^2+\beta \left( x_{0}^2x_{i}^2-x_{0}^2x_{j}^2\right) \\&=\beta x_{0}^2x_{i}^2+(1-\beta )x_{0}^2x_{j}^2\\&\ge \beta (1-\beta )\left( x_{0}x_{i}-x_{0}x_{j}\right) ^{2}\\&=\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\&=\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned}$$

      Since \(x_{0}x_{i}>0\) and \(x_{0}x_{j}<0\), we have \(x_{0}x_{i}-x_{0}x_{j}>0\), \(x_{0}x_{i}+x_{0}x_{j}>0\), and

      $$\begin{aligned} \begin{aligned} x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right)&\le -x_{0}x_{j}\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \\&=2\left( x_{0}^2x_{j}^2+x_{0}x_{i}(-x_{0}x_{j})\right) \\&\le 2\left( x_{0}^2x_{j}^2+x_{0}^2x_{i}^2)\right) \\&=2\left( x_{0}^2x_{j}^2+x_{0}^2x_{i}^2-x_{0}^2x_{j}^2+x_{0}^2x_{j}^2)\right) \\&=2X(i,j)+4C(i,j). \end{aligned} \end{aligned}$$

      Therefore,

      $$\begin{aligned} \begin{aligned} -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$

      Finally,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j}-2x_{i} x_{j}\right) \\&\quad -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    6. Case 3.6.

      \(x_{0}x_{i}> 0\), \(x_{0}x_{j}< 0\), and \(|x_{0}x_{i}|\le |x_{0}x_{j}|\) (\( x_{0}x_{i}\le -x_{0}x_{j}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)=&x_{0}^2x_{i}^2,\\ X(i,j)=&\mathbb {P}\left\{ |x_{0}x_{i}|\le \sqrt{t}<-x_{0}x_{j}\right\} =x_{0}^2x_{j}^2-x_{0}^2x_{i}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&= x_{0}^2x_{i}^2+\beta \left( x_{0}^2x_{j}^2-x_{0}^2x_{i}^2\right) \\&=\beta x_{0}^2x_{j}^2+(1-\beta )x_{0}^2x_{i}^2\\&\ge \beta (1-\beta )\left( x_{0}x_{j}-x_{0}x_{i}\right) ^{2}\\&=\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\&=\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j} -2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned} \end{aligned}$$

      It is easy to get \(x_{0}x_{i}-x_{0}x_{j}>0\), \(x_{0}x_{i}+x_{0}x_{j}>0\), and

      $$\begin{aligned} \begin{aligned} x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right)&\le x_{0}x_{i}\left( 2x_{0} x_{i}-2x_{0}x_{j}\right) \\&= 2\left( x_{0}^2x_{i}^2+x_{0}x_{i}(-x_{0}x_{j})\right) \\&\le 2\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2\right) \\&= 2\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-x_{0}^2x_{i}^2+x_{0}^2x_{i}^2\right) \\&= 2X(i,j)+4C(i,j). \end{aligned} \end{aligned}$$

      Therefore,

      $$\begin{aligned} \begin{aligned} -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$

      Finally,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2 +2x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    7. Case 3.7.

      \(x_{0}x_{i}< 0\), \(x_{0}x_{j}> 0\), and \(|x_{0}x_{j}|\le |x_{0}x_{i}|\) (\(x_{0}x_{j}\le -x_{0}x_{i}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)&=x_{0}^2x_{j}^2,\\ X(i,j)&=\mathbb {P}\left\{ |x_{0}x_{j}|\le \sqrt{t}<-x_{0}x_{i}\right\} =x_{0}^2x_{i}^2-x_{0}^2x_{j}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} C(i,j)+\beta X(i,j)&=x_{0}^2x_{j}^2+\beta \left( x_{0}^2x_{i}^2 -x_{0}^2x_{j}^2\right) \\&=\beta x_{0}^2x_{i}^2+(1-\beta )x_{0}^2x_{j}^2\\&\ge \beta (1-\beta )\left( x_{0}x_{i}-x_{0}x_{j}\right) ^{2}\\&=\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\&=\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0} x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned}$$

      Since \(x_{0}x_{i}+x_{0}x_{j}\le 0\), we have

      $$\begin{aligned}&-\beta (1-\beta ) x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge 0\\&\quad \ge -\beta (1-\beta ) x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \left( x_{0} x_{i}+x_{0}x_{j}\right) \\&\quad \ge -\beta (1-\beta )2\left( x_{0}^2x_{i}^2-x_{0}^2x_{j}^2\right) \\&\quad =-\beta (1-\beta )2X(i,j)\\&\quad \ge -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned}$$

      Therefore,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&\ge \beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2 x_{0}x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$
    8. Case 3.8.

      \(x_{i}x_{0}< 0\), \(x_{j}x_{0}> 0\), and \(|x_{0}x_{i}| \le |x_{0}x_{j}|\) (\( -x_{0}x_{i}\le x_{0}x_{j}\)). Then, we have

      $$\begin{aligned} \begin{aligned} C(i,j)&=x_{0}^2x_{i}^2,\\ X(i,j)&=\mathbb {P}\left\{ |x_{0}x_{i}|\le \sqrt{t}<-x_{0}x_{j}\right\} =x_{0}^2x_{j}^2-x_{0}^2x_{i}^2, \end{aligned} \end{aligned}$$

      and

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)&=x_{0}^2x_{i}^2+\beta \left( x_{0}^2x_{j}^2-x_{0}^2 x_{i}^2\right) \\&=\beta x_{0}^2x_{j}^2+(1-\beta )x_{0}^2x_{i}^2\\&\ge \beta (1-\beta )\left( x_{0}x_{j}-x_{0}x_{i}\right) ^{2}\\&=\beta (1-\beta )\left( x_{0}^2x_{i}^2+x_{0}^2x_{j}^2-2x_{0}^2x_{i}x_{j}\right) \\&=\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0}x_{i}-2x_{0}x_{j}-2 x_{i}x_{j}\right) \\&\quad -\beta (1-\beta )x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) . \end{aligned} \end{aligned}$$

      Since \(x_{0}x_{i}+x_{0}x_{j}\ge 0\) and \(-\left( x_{0}x_{i}+x_{0}x_{j}\right) \le 0\), we have

      $$\begin{aligned}&-\beta (1-\beta ) x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \ge 0\\&\quad \ge -\beta (1-\beta ) x_{0}^2\left( 2x_{0}x_{i}-2x_{0}x_{j}\right) \left[ -\left( x_{0} x_{i}+x_{0}x_{j}\right) \right] \\&\quad \ge -\beta (1-\beta )2\left( x_{0}^2x_{j}^2-x_{0}^2x_{i}^2\right) \\&\quad =-\beta (1-\beta )2X(i,j)\\&\quad \ge -\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned}$$

      Therefore,

      $$\begin{aligned} \begin{aligned} C(i,j)+\beta X(i,j)\ge \,&\beta (1-\beta )x_{0}^2\left( x_{i}^2+x_{j}^2+2x_{0} x_{i}-2x_{0}x_{j}-2x_{i}x_{j}\right) \\&-\,\beta (1-\beta )\left[ 2X(i,j)+4C(i,j)\right] . \end{aligned} \end{aligned}$$

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Zhang, Z., Du, D., Wu, C. et al. A spectral partitioning algorithm for maximum directed cut problem. J Comb Optim 42, 373–395 (2021). https://doi.org/10.1007/s10878-018-0369-4

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