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Intersection Cuts for Polynomial Optimization

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Integer Programming and Combinatorial Optimization (IPCO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11480))

Abstract

We consider dynamically generating linear constraints (cutting planes) to tighten relaxations for polynomial optimization problems. Many optimization problems have feasible set of the form \(S \cap P\), where S is a closed set and P is a polyhedron. Integer programs are in this class and one can construct intersection cuts using convex “forbidden” regions, or S-free sets. Here, we observe that polynomial optimization problems can also be represented as a problem with linear objective function over such a feasible set, where S is the set of real, symmetric matrices representable as outer-products of the form \(xx^T\). Accordingly, we study outer-product-free sets and develop a thorough characterization of several (inclusion-wise) maximal intersection cut families. In addition, we present a cutting plane approach that guarantees polynomial-time separation of an extreme point in \(P\setminus S\) using our outer-product-free sets. Computational experiments demonstrate the promise of our approach from the point of view of strength and speed.

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Notes

  1. 1.

    This BoxQP relaxation only adds the “diagonal” McCormick estimates \(X_{ii} \le x_i\).

References

  1. Andersen, K., Louveaux, Q., Weismantel, R.: An analysis of mixed integer linear sets based on lattice point free convex sets. Math. Oper. Res. 35(1), 233–256 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  2. Andersen, K., Louveaux, Q., Weismantel, R., Wolsey, L.A.: Inequalities from two rows of a simplex tableau. In: Fischetti, M., Williamson, D.P. (eds.) IPCO 2007. LNCS, vol. 4513, pp. 1–15. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72792-7_1

    Chapter  Google Scholar 

  3. Anstreicher, K.M.: Semidefinite programming versus the reformulation linearization technique for nonconvex quadratically constrained quadratic programming. J. Glob. Optim. 43, 471–484 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  4. Atamtürk, A., Narayanan, V.: Conic mixed-integer rounding cuts. Math. Program. 122, 1–20 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Averkov, G.: On finite generation and infinite convergence of generalized closures from the theory of cutting planes. arXiv preprint arXiv:1106.1526 (2011)

  6. Balas, E.: Intersection cuts—a new type of cutting planes for integer programming. Oper. Res. 19(1), 19–39 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bao, X., Sahinidis, N.V., Tawarmalani, M.: Multiterm polyhedral relaxations for nonconvex, quadratically constrained quadratic programs. Optim. Methods Softw. 24(4–5), 485–504 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Basu, A., Conforti, M., Cornuéjols, G., Zambelli, G.: Maximal lattice-free convex sets in linear subspaces. Math. Oper. Res. 35(3), 704–720 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Basu, A., Conforti, M., Cornuéjols, G., Zambelli, G.: Minimal inequalities for an infinite relaxation of integer programs. SIAM J. Discrete Math. 24(1), 158–168 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. Belotti, P., Góez, J.C., Pólik, I., Ralphs, T.K., Terlaky, T.: On families of quadratic surfaces having fixed intersections with two hyperplanes. Discrete Appl. Math. 161(16–17), 2778–2793 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bienstock, D., Michalka, A.: Cutting-planes for optimization of convex functions over nonconvex sets. SIAM J. Optim. 24, 643–677 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  12. Bienstock, D., Chen, C., Muñoz, G.: Outer-product-free sets for polynomial optimization and oracle-based cuts. arXiv preprint arXiv:1610.04604 (2016)

  13. Bonami, P., Günlük, O., Linderoth, J.: Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods. Math. Program. Comput. 10(3), 333–382 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  14. Borozan, V., Cornuéjols, G.: Minimal valid inequalities for integer constraints. Math. Oper. Res. 34(3), 538–546 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Burer, S.: Optimizing a polyhedral-semidefinite relaxation of completely positive programs. Math. Program. Comput. 2(1), 1–19 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Chen, C., Atamtürk, A., Oren, S.S.: A spatial branch-and-cut method for nonconvex QCQP with bounded complex variables. Math. Program. 165(2), 549–577 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  17. Conforti, M., Cornuéjols, G., Daniilidis, A., Lemaréchal, C., Malick, J.: Cut-generating functions and S-free sets. Math. Oper. Res. 40(2), 276–391 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. Cornuéjols, G., Wolsey, L., Yıldız, S.: Sufficiency of cut-generating functions. Math. Program. 152, 1–9 (2013)

    MathSciNet  MATH  Google Scholar 

  19. Dax, A.: Low-rank positive approximants of symmetric matrices. Adv. Linear Algebra Matrix Theory 4(3), 172–185 (2014)

    Article  Google Scholar 

  20. Dey, S.S., Wolsey, L.A.: Lifting integer variables in minimal inequalities corresponding to lattice-free triangles. In: Lodi, A., Panconesi, A., Rinaldi, G. (eds.) IPCO 2008. LNCS, vol. 5035, pp. 463–475. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68891-4_32

    Chapter  MATH  Google Scholar 

  21. Dey, S.S., Wolsey, L.A.: Constrained infinite group relaxations of MIPs. SIAM J. Optim. 20(6), 2890–2912 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  22. Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211–218 (1936)

    Article  MATH  Google Scholar 

  23. Fischetti, M., Ljubić, I., Monaci, M., Sinnl, M.: A new general-purpose algorithm for mixed-integer bilevel linear programs. Oper. Res. 65(6), 1615–1637 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  24. Floudas, C.A., et al.: Handbook of Test Problems in Local and Global Optimization, vol. 33. Springer, Boston (2013). https://doi.org/10.1007/978-1-4757-3040-1

    Book  Google Scholar 

  25. Freund, R.M., Orlin, J.B.: On the complexity of four polyhedral set containment problems. Math. Program. 33(2), 139–145 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  26. Ghaddar, B., Vera, J.C., Anjos, M.F.: A dynamic inequality generation scheme for polynomial programming. Math. Program. 156(1–2), 21–57 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  27. Gomory, R.E., Johnson, E.L.: Some continuous functions related to corner polyhedra. Math. Program. 3(1), 23–85 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  28. Guennebaud, G., Jacob, B., et al.: Eigen v3 (2010). http://eigen.tuxfamily.org

  29. Hillestad, R.J., Jacobsen, S.E.: Reverse convex programming. Appl. Math. Optim. 6(1), 63–78 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  30. Kılınç-Karzan, F.: On minimal valid inequalities for mixed integer conic programs. Math. Oper. Res. 41(2), 477–510 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  31. Kocuk, B., Dey, S.S., Sun, X.A.: Matrix minor reformulation and SOCP-based spatial branch-and-cut method for the AC optimal power flow problem. Math. Program. Comput. 10(4), 557–596 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  32. Krishnan, K., Mitchell, J.E.: A unifying framework for several cutting plane methods for semidefinite programming. Optim. Methods Softw. 21, 57–74 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  33. Lasserre, J.B.: Global optimization with polynomials and the problem of moments. SIAM J. Optim. 11, 796–817 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  34. Laurent, M.: Sums of squares, moment matrices and optimization over polynomials. In: Putinar, M., Sullivant, S. (eds.) Emerging Applications of Algebraic Geometry, pp. 157–270. Springer, New York (2009). https://doi.org/10.1007/978-0-387-09686-5_7

    Chapter  Google Scholar 

  35. Locatelli, M., Schoen, F.: On convex envelopes for bivariate functions over polytopes. Math. Program. 144, 1–27 (2013)

    MathSciNet  MATH  Google Scholar 

  36. Lovász, L.: Geometry of numbers and integer programming. In: Mathematical Programming: Recent Developments and Applications, pp. 177–210 (1989)

    Google Scholar 

  37. Luedtke, J., Namazifar, M., Linderoth, J.: Some results on the strength of relaxations of multilinear functions. Math. Program. 136(2), 325–351 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  38. McCormick, G.P.: Computability of global solutions to factorable nonconvex programs: part I - convex underestimating problems. Math. Program. 10(1), 147–175 (1976)

    Article  MATH  Google Scholar 

  39. Meeraus, A.: GLOBALLib. http://www.gamsworld.org/global/globallib.htm

  40. Mirsky, L.: Symmetric gauge functions and unitarily invariant norms. Q. J. Math. 11(1), 50–59 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  41. Misener, R., Floudas, C.A.: Global optimization of mixed-integer quadratically-constrained quadratic programs (MIQCQP) through piecewise-linear and edge-concave relaxations. Math. Program. 136(1), 155–182 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  42. Modaresi, S., Kılınç, M.R., Vielma, J.P.: Intersection cuts for nonlinear integer programming: convexification techniques for structured sets. Math. Program. 155, 1–37 (2015)

    MathSciNet  MATH  Google Scholar 

  43. MOSEK ApS: The MOSEK Fusion API for C++ 8.1.0.63 (2018). https://docs.mosek.com/8.1/cxxfusion/index.html

  44. Qualizza, A., Belotti, P., Margot, F.: Linear programming relaxations of quadratically constrained quadratic programs. In: Lee, J., Leyffer, S. (eds.) Mixed Integer Nonlinear Programming, pp. 407–426. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-1927-3_14

    Chapter  MATH  Google Scholar 

  45. Rikun, A.D.: A convex envelope formula for multilinear functions. J. Glob. Optim. 10(4), 425–437 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  46. Saxena, A., Bonami, P., Lee, J.: Convex relaxations of non-convex mixed integer quadratically constrained programs: extended formulations. Math. Program. 124, 383–411 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  47. Saxena, A., Bonami, P., Lee, J.: Convex relaxations of non-convex mixed integer quadratically constrained programs: projected formulations. Math. Program. 130, 359–413 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  48. Sen, S., Sherali, H.D.: Nondifferentiable reverse convex programs and facetial convexity cuts via a disjunctive characterization. Math. Program. 37(2), 169–183 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  49. Sherali, H.D., Fraticelli, B.M.P.: Enhancing RLT relaxations via a new class of semidefinite cuts. J. Glob. Optim. 22, 233–261 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  50. Shor, N.Z.: Quadratic optimization problems. Sov. J. Circ. Syst. Sci. 25, 6 (1987)

    MathSciNet  Google Scholar 

  51. Tardella, F.: Existence and sum decomposition of vertex polyhedral convex envelopes. Optim. Lett. 2, 363–375 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  52. Tawarmalani, M., Richard, J.P.P., Xiong, C.: Explicit convex and concave envelopes through polyhedral subdivisions. Math. Program. 138, 1–47 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  53. Tawarmalani, M., Sahinidis, N.V.: Convex extensions and envelopes of lower semi-continuous functions. Math. Program. 93, 247–263 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  54. Tuy, H.: Concave programming under linear constraints. Sov. Math. 5, 1437–1440 (1964)

    MATH  Google Scholar 

  55. Vandenbussche, D., Nemhauser, G.: A branch-and-cut algorithm for nonconvex quadratic programs with box constraints. Math. Program. 102(3), 559–575 (2005)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments. This research was partly supported by award ONR N00014-16-1-2889, Conicyt Becas Chile 72130388 and The Institute for Data Valorisation (IVADO).

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Correspondence to Gonzalo Muñoz .

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Appendix

Appendix

Proof

(Theorem 4). The OPF property is given by Lemma 2, so maximality remains. Let C be a set described by (5a) or (5b). It suffices to construct, for every symmetric matrix \(\bar{X} \not \in C\), such that \(Z-\bar{X}\in \text {int}(C)\). This implies \(Z \in \text {int} ( \text {conv}(C\cup \bar{X}))\). Denote the submatrices of \(\bar{X}, Z\):

Furthermore, for convenience let us define the following:

Construction for (5a): Suppose \(\bar{X}\) violates (5a). We propose the following:

$$\begin{aligned} a_Z = \bar{q} + \lambda _1\Vert \bar{q}, \bar{r}\Vert _2,\, b_Z = \bar{r}+ \lambda _2\Vert \bar{q},\bar{r}\Vert _2,&\, c_Z = \bar{r} - \lambda _2\Vert \bar{q},\bar{r}\Vert _2,\, d_Z = -\bar{q} + \lambda _1\Vert \bar{q},\bar{r}\Vert _2 \\ \Longrightarrow \, \lambda _1(\bar{a}+\bar{d})/2+\lambda _2(\bar{b}-\bar{c})/2 \,&< \, \Vert (\bar{b}+\bar{c})/2,(\bar{a}-\bar{d})/2\Vert _2 \nonumber \\&= \, \lambda _1(a_Z+ d_Z)/2+\lambda _2(b_Z-c_Z)/2 \nonumber \end{aligned}$$
(6)

where the last equality follows from \(\lambda _1^2 + \lambda _2^2 = 1\). This implies

$$\begin{aligned} \lambda _1((a_Z-\bar{a})+ (d_Z- \bar{d}))/2 +\lambda _2((b_Z-\bar{b})-(c_Z-\bar{c}))/2 > 0 \end{aligned}$$

and since \( \Vert ((b_Z-\bar{b})+(c_Z-\bar{c}))/2,((a_Z-\bar{a})-(d_Z-\bar{d}))/2\Vert _2 = 0 \), we conclude \(Z-\bar{X} \in \text {int}(C)\).

Construction for (5b): If \(\bar{X}\) violates (5b), we use the following construction:

$$\begin{aligned} a_Z = \bar{p} + \lambda _2\Vert \bar{p},\bar{s}\Vert _2, b_Z = \bar{s}+ \lambda _1\Vert \bar{p},\bar{s}\Vert _2,&\, c_Z = -\bar{s} + \lambda _1\Vert \bar{p},\bar{s}\Vert _2, d_Z = \bar{p} - \lambda _2\Vert \bar{p},\bar{s}\Vert _2. \\ \Longrightarrow \lambda _1(\bar{b}+\bar{c})/2+\lambda _2(\bar{a}-\bar{d})/2 \,&< \, \Vert (\bar{a}+\bar{d})/2,(\bar{b}-\bar{c})/2\Vert _2 \\ {}&= \, \lambda _1(b_Z+ c_Z)/2+\lambda _2(a_Z-d_Z)/2, \\ \Longrightarrow \, \lambda _1((b_Z-\bar{b})+(c_Z-\bar{c}))/2&+\lambda _2((a_Z-\bar{a})- (d_Z- \bar{d}))/2 > 0. \end{aligned}$$

We conclude \(Z-\bar{X} \in \text {int}(C)\) as before, since \( \Vert ((a_Z-\bar{a})+(d_Z-\bar{d}))/2,((b_Z-\bar{b})-(c_Z-\bar{c}))/2\Vert _2 = 0.\) It remains to set the other entries of Z and to show it is an outer product.

Claim

For each condition (i)–(viii), \(a_Zd_Z=b_Zc_Z\) and all diagonal elements among \(a_Z,b_Z,c_Z,d_Z\) are nonnegative.

Proof: First consider conditions (i)–(iv). By construction of (6):

$$\begin{aligned}&a_Zd_Z\, =\, -\bar{q}^2 + \lambda _1^2\Vert \bar{q},\bar{r}\Vert ^2_2 \, = \, \bar{r}^2-\lambda _2^2\Vert \bar{q},\bar{r}\Vert _2^2\, = \, b_Zc_Z. \end{aligned}$$

The second equality is derived from the following identity:

$$\begin{aligned}&\Vert \bar{q},\bar{r}\Vert _2^2 = \bar{q}^2+\bar{r}^2 \iff \, -\bar{q}^2 + \lambda _1^2\Vert \bar{q},\bar{r}\Vert _2^2 = \bar{r}^2-\lambda _2^2\Vert \bar{q},\bar{r}\Vert _2^2. \end{aligned}$$

Nonnegativity of diagonal elements follows from \(\Vert \bar{q}, \bar{r}\Vert _2 \ge \max \{|\bar{q}|,|\bar{r}|\}\). In case (i) only \(a_Z\) or \(d_Z\) can be diagonal elements, and they are both nonnegative. The other cases can be directly verified. Similarly, for conditions (v)–(viii):

$$\begin{aligned}&a_Zd_Z = \bar{p}^2 - \lambda _2^2\Vert \bar{p},\bar{s}\Vert ^2_2 \, = \, -\bar{s}^2+\lambda _1^2\Vert \bar{p},\bar{s}\Vert _2^2 \, = \, b_Zc_Z. \end{aligned}$$

The second equality is derived from the following identity:

$$\begin{aligned} \Vert \bar{p},\bar{s}\Vert _2^2 = \bar{p}^2+\bar{s}^2&\iff \, -\bar{s}^2 + \lambda _1^2\Vert \bar{p},\bar{s}\Vert _2^2 = \bar{p}^2-\lambda _2^2\Vert \bar{p},\bar{s}\Vert _2^2. \end{aligned}$$

Nonnegativity of diagonal elements follows from the same argument as before, by using the fact that \(\Vert \bar{p}, \bar{s}\Vert _2 \ge \max \{|\bar{p}|,|\bar{s}|\}\).    \(\blacksquare \)

To maintain symmetry we set \(Z_{i_1,j_1}=Z_{j_1,i_1}\), \(Z_{i_1,j_2}=Z_{j_2,i_1}, Z_{i_2,j_1}=Z_{j_1,i_2}\), \(Z_{i_2,j_2}=Z_{j_2,i_2}\). Now denote \(\ell = [i_1,i_2,j_1,j_2]\). If \(a_Z=b_Z=c_Z=d_Z=0\), then we simply set all other entries of Z equal to zero and so Z is the outer-product of the vector of zeroes. Otherwise, consider the following cases.

Case 1: \(\ell \) has 4 unique entries. Suppose w.l.o.g we have an upper-triangular entry \((i_1<i_2<j_1<j_2)\) and furthermore suppose that \(b_Z\) is nonzero. Then set

and set all remaining entries of Z to zero. Other orderings of indices or the use of a different nonzero entry is handled by relabeling/rearranging column/row order.

Case 2: \(\ell \) has three unique entries. Then, exactly one of \(a_Z,b_Z,c_Z,d_Z\) is a diagonal entry, and so cases (i)–(iii), (v)–(vii) apply. If in any of these cases \(a_Z\) or \(d_Z\) is on the diagonal, by construction \(|b_Z|=|c_Z|\). As \(a_Zd_Z=b_Zc_Z\), we have \(b_Z=c_Z=0\) iff exactly one of \(a_Z\) or \(d_Z\) is zero. Likewise, if \(b_Z\) or \(c_Z\) is a diagonal element, then \(|a_Z|=|d_Z|\) and so \(a_Z=d_Z=0\) iff exactly one of \(b_Z\) or \(c_Z\) are zero.

Suppose \(a_Z\) is a nonzero diagonal entry. We propose:

$$Z_{\ell '}= \left[ \begin{array}{ccc} a_Z &{} b_Z &{} c_Z \\ b_Z&{} b_Z^2/a_Z&{} d_Z\\ c_Z&{} d_Z&{} c_Z^2/a_Z \end{array}\right] $$

where \(\ell '\) are the unique entries of \(\ell \). If \(a_Z=0\) and on the diagonal, then we replace \(b_Z^2/a_Z\) and \(c_Z^2/a_Z\) with \(|d_Z|\). If \(b_Z,c_Z\) or \(d_Z\) is on the diagonal, we use the same construction but with relabeling/rearranging column/row order.

Case 3: \(\ell \) has two unique entries. All remaining entries of Z are set to zero.

For all cases, our construction ensures that all diagonal entries of Z are nonnegative, and all \(2\times 2\) minors are zero; by Proposition 1, Z is an outer-product.    \(\square \)

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Bienstock, D., Chen, C., Muñoz, G. (2019). Intersection Cuts for Polynomial Optimization. In: Lodi, A., Nagarajan, V. (eds) Integer Programming and Combinatorial Optimization. IPCO 2019. Lecture Notes in Computer Science(), vol 11480. Springer, Cham. https://doi.org/10.1007/978-3-030-17953-3_6

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