×

A simultaneous diagonalization based SOCP relaxation for portfolio optimization with an orthogonality constraint. (English) Zbl 1517.90115

Summary: The portfolio rebalancing with transaction costs plays an important role in both theoretical analyses and commercial applications. This paper studies a standard portfolio problem that is subject to an additional orthogonality constraint guaranteeing that buying and selling a same security do not occur at the same time point. Incorporating the orthogonality constraint into the portfolio problem leads to a quadratic programming problem with linear complementarity constraints. We derive an enhanced simultaneous diagonalization based second order cone programming (ESDSOCP) relaxation by taking advantage of the feature that the objective and constraint matrices are commutative. The ESDSOCP relaxation has lower computational complexity than the semi-definite programming (SDP) relaxation, and it is proved to be as tight as the SDP relaxation. It is worth noting that the original simultaneous diagonalization based second order cone programming relaxation (SDSOCP) is only guaranteed to be as tight as the SDP relaxation on condition that the objective matrix is positive definite. Note that the objective matrix in this paper is positive semidefinite (while not positive definite), thus the ESDSOCP relaxation outperforms the original SDSOCP relaxation. We further design a branch and bound algorithm based on the ESDSOCP relaxation to find the global optimal solution and computational results illustrate the effectiveness of the proposed algorithm.

MSC:

90C26 Nonconvex programming, global optimization
90C33 Complementarity and equilibrium problems and variational inequalities (finite dimensions) (aspects of mathematical programming)
90C34 Semi-infinite programming
91G10 Portfolio theory
Full Text: DOI

References:

[1] Arnott, RD; Wagner, WH, The measurement and control of trading costs, Financ. Anal. J., 46, 6, 73-80 (1990) · doi:10.2469/faj.v46.n6.73
[2] Baviera, R.; Bianchi, G., Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach, Optim. Lett., 81, 469-491 (2021) · Zbl 1478.91167
[3] Ben-Tal, A.; Den Hertog, D., Hidden conic quadratic representation of some nonconvex quadratic optimization problems, Math. Program., 143, 1-2, 1-29 (2014) · Zbl 1295.90036 · doi:10.1007/s10107-013-0710-8
[4] Best, MJ; Hlouskova, J., Portfolio selection and transactions costs, Comput. Optim. Appl., 24, 95-116 (2003) · Zbl 1031.90049 · doi:10.1023/A:1021806200854
[5] Burer, S.; Kim, S.; Kojima, M., Faster, but weaker, relaxations for quadratically constrained quadratic programs, Comput. Optim. Appl., 59, 27-45 (2014) · Zbl 1303.90077 · doi:10.1007/s10589-013-9618-8
[6] Braun, S.E.: Solving a quadratic programming problem subject to orthogonality constraints. Ph.D. thesis, Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY (2001)
[7] Braun, S.; Mitchell, JE, A semidefinite programming heuristic for quadratic programming problems with complementarity constraints, Comput. Optim. Appl., 31, 1, 5-29 (2005) · Zbl 1114.90158 · doi:10.1007/s10589-005-1014-6
[8] Dinh, TP; Thi, H.; Pham, VN; Niu, YS, DC programming approaches for discrete portfolio optimization under concave transaction costs, Optim. Lett., 10, 2, 261-282 (2015) · Zbl 1343.90072 · doi:10.1007/s11590-015-0931-2
[9] Gao, J.; Li, D., Optimal cardinality constrained portfolio selection, Oper. Res., 61, 3, 745-761 (2013) · Zbl 1273.91423 · doi:10.1287/opre.2013.1170
[10] Gillett, P.L.: Semidefinite programming approaches and software tools for quadratic programs with linear complementarity constraints. Ph.D. thesis, Engineering Mathematics, Polytechnique Montreal, Quebec (2016)
[11] Guo, S.; Gu, JW; Ching, WK, Adaptive online portfolio selection with transaction costs, Eur. J. Oper. Res., 295, 1074-1086 (2021) · Zbl 1490.91182 · doi:10.1016/j.ejor.2021.03.023
[12] González-Díaz, J.; González-Rodríguez, B.; Leal, M.; Puerto, J., Global optimization for bilevel portfolio design: economic insights from the Dow Jones index, Omega Int. J. Manag. Sci., 102 (2021) · doi:10.1016/j.omega.2020.102353
[13] Katsikis, VN; Mourtas, SD; Stanimirovi, PS; Li, S.; Gao, X., Time-varying mean-variance portfolio selection problem solving via LVI-PDNN, Comput. Oper. Res., 138 (2022) · Zbl 1511.91127 · doi:10.1016/j.cor.2021.105582
[14] Kim, S.; Kojima, M., Second order cone programming relaxation of nonconvex quadratic optimization problems, Optim. Methods Softw., 15, 201-224 (2001) · Zbl 1109.90327 · doi:10.1080/10556780108805819
[15] Kolm, PN; Tütüncü, R.; Fabozzi, FJ, 60 Years of portfolio optimization: practical challenges and current trends, Eur. J. Oper. Res., 234, 2, 356-371 (2014) · Zbl 1304.91200 · doi:10.1016/j.ejor.2013.10.060
[16] Konno, H.; Wijayanayake, KA, Portfolio optimization under D.C. transaction costs and minimal transaction unit constraints, J. Glob. Optim., 22, 137-152 (2002) · Zbl 1045.91022 · doi:10.1023/A:1013850928936
[17] Landsman, Z.; Makov, U., Minimization of a function of a quadratic functional with application to optimal portfolio selection, J. Optim. Theory Appl., 170, 1, 308-322 (2016) · Zbl 1346.90668 · doi:10.1007/s10957-015-0856-z
[18] Linderoth, J., A simplicial branch-and-bound algorithm for solving quadratically constrained quadratic programs, Math. Program., 103, 2, 251-282 (2005) · Zbl 1099.90039 · doi:10.1007/s10107-005-0582-7
[19] Lobo, MS; Fazel, M.; Boyd, S., Portfolio optimization with linear and fixed transaction costs, Ann. Oper. Res., 152, 341-365 (2007) · Zbl 1132.91474 · doi:10.1007/s10479-006-0145-1
[20] Markowitz, HM, Portfolio selection, J. Finance, 7, 1, 77-91 (1952)
[21] Marzban, S.; Mahootchi, M.; Khamseh, AA, Developing a multi-period robust optimization model considering American style options, Ann. Oper. Res., 223, 1, 305-320 (2015) · Zbl 1325.90099 · doi:10.1007/s10479-013-1461-x
[22] Mittelmann, H.D.: Several SDP-codes on sparse and other SDP problems. http://plato.asu.edu/ftp/sparse_sdp.html. Accessed 27 Dec 2022
[23] Newcomb, RW, On the simultaneous diagonalization of two semi-definite matrices, Q. Appl. Math., 19, 2, 144-146 (1961) · Zbl 0103.25202 · doi:10.1090/qam/124336
[24] Wang, J.; Lu, J.; Feng, Y., Congruence diagonalization of two hermite matrices simultaneously, Int. J. Algebra, 4, 23, 1119-1125 (2010) · Zbl 1223.15044
[25] Ying, H.; Ng, K.; Huang, B.; Huang, H., Portfolio optimization with transaction costs: a two-period mean-variance model, Ann. Oper. Res., 233, 1, 135-156 (2014) · Zbl 1358.91093
[26] Yoshimoto, A., The mean-variance approach to portfolio optimization subject to transaction costs, J. Oper. Res. Soc. Jpn., 39, 99-117 (1996) · Zbl 0851.90014
[27] Zhang, Y.; Xiang, L.; Guo, S., Portfolio selection problems with Markowitz’s mean-variance framework: a review of literature, Fuzzy Optim. Decis. Mak., 17, 2, 1-34 (2017)
[28] Zhou, J.; Xu, Z., A simultaneous diagonalization based SOCP relaxation for convex quadratic programs with linear complementarity constraints, Optim. Lett., 13, 7, 1615-1630 (2019) · Zbl 1431.90115 · doi:10.1007/s11590-018-1337-8
[29] Zhou, J.; Chen, S.; Yu, S.; Tian, Y., A simultaneous diagonalization based quadratic convex reformulation for nonconvex quadratically constrained quadratic program, Optimization, 71, 9, 2529-2545 (2022) · Zbl 1501.90064 · doi:10.1080/02331934.2020.1865347
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.