[1] Markowitz, H.: Portfolio selection. J. Finance 7(1), 77–91 (1952)
[2] Jondeau, E., Rockinger, M.: Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements. J. Econ. Dyn. Control 27(10), 1699–1737 (2013)
[3] Singleton, J.C., Wingender, J.: Skewness persistence in common stock returns. J. Financ. Quanti. Anal. 21(3), 335–341 (1986)
[4] Harvey, C.R., Siddique, A.: Conditional skewness in asset pricing tests. J. Financ. 55(3), 1263–1295 (2000)
[5] Konno, H., Suzuki, K.: A mean-variance-skewness portfolio optimization model. J. Op. Res. Soc. Jpn 38(2), 173–187 (1995)
[6] Davies, D.J., Kat, H.M., Lu, S.: Fund of hedge funds portfolio selection: amultiple-objective approach. J. Deriv. Hedge Funds 15(2), 91–115 (2009)
[7] Lasserre, J.B.: Global optimization with polynomials and the problem of moments. SIAM J. Optim. 11(3), 796–817 (2001)
[8] Laurent, M.: Sums of squares, moment matrices and optimization over polynomials. IMA Vol. Math. Appl. 149, pp. 157-270. Springer, New York (2009)
[9] Nie, J., Yang, L., Zhong, S.: Stochastic polynomial optimization. Optim. Methods Softw. 35(2), 329–347 (2020)
[10] Markowitz, H.: Mean-variance approximations to expected utility. European J. Oper. Res. 234(2), 346–355 (2014)
[11] Rubinstein, M.: Markowitz’s “portfolio selection”: A fifty-year retrospective. J. Financ. 57(3), 1041– 1045 (2002)
[12] Steinbach, M.C.: Markowitz revisited: Mean-variance models in financial portfolio analysis. SIAM Rev. 43(1), 31–85 (2001)
[13] Levy, H., Markowitz, H.M.: Approximating expected utility by a function of mean and variance. Am. Econ. Rev. 69(3), 308–317 (1979)
[14] Maringer, D.: Parpas, P,: Global optimization of higher order moments in portfolio selection. J. Global Optim. 43(2–3), 219–230 (2009)
[15] Black, F., Litterman, R.: Global portfolio optimization. Financ. Anal. J. 48(5), 28–43 (1992)
[16] Goldfarb, D., Iyengar, G.: Robust portfolio selection problems. Math. Oper. Res. 28(1), 1–38 (2003)
[17] Kolm, P.N., Tütüncü, R., Fabozzi, F.J.: 60 years of portfolio optimization: Practical challenges and current trends. European J. Oper. Res. 234(2), 356–371 (2014)
[18] Lan, G.: First-order and stochastic optimization methods for machine learning. Springer (2020)
[19] Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on Stochastic Programming: Modeling and Theory. SIAM (2021)
[20] Putinar, M.: Positive polynomials on compact semi-algebraic sets. Indiana Univ. Math. J. 42(3), 969–984 (1993)
[21] Nie, J.: Certifying convergence of Lasserre’s hierarchy via flat truncation. Math. Program. 142(1–2), 385–510 (2013)
[22] Nie, J.: Optimality conditions and finite convergence of Lasserre’s hierarchy. Math. Program. 146(1– 2), 97–121 (2014)
[23] Fan, J., Nie, J., Zhou, A.: Tensor eigenvalue complementarity problems. Math. Program. 170(2), 507–539 (2018)
[24] Guo, B., Nie, J., Yang, Z.: Learning diagonal Gaussian mixture models and incomplete tensor decompositions. Vietnam J. Math. 50(2), 421–446 (2022)
[25] Nie, J., Tang, X.: Convex generalized Nash equilibrium problems and polynomial optimization. Math. Program. 198(2), 1485–1518 (2023)
[26] Nie, J., Yang, L., Zhong, S., Zhou, G.: Distributionally robust optimization with moment ambiguity sets. J. Sci. Comput. 94(12), 1–27 (2023)
[27] Huang, L., Nie, J., Yuan, Y.X.: Homogenization for polynomial optimization with unbounded sets. Math. Program. 200(1), 105–145 (2023)
[28] Qu, Z., Tang, X.: A correlative sparse Lagrange multiplier expression relaxation for polynomial optimization (2022). arXiv:2208.03979
[29] Nie, J., Yang, Z.: The multi-objective polynomial optimization (2021). arXiv:2108.04336
[30] Nie, J.: Moment and Polynomial Optimization. SIAM (2023)
[31] Henrion, D., Korda, M., Lasserre, J.B.: The moment-SOS hierarchy-lectures in probability, statistics, computational geometry. World Scientific, Control and Nonlinear PDEs (2020)
[32] Lasserre, J.B.: An introduction to polynomial and semi-algebraic optimization. Cambridge University Press, Cambridge (2015)
[33] Curto, R.E., Fialkow, L.A.: Truncated K-moment problems in several variables. J. Operator Theory 54(1), 189–226 (2005)
[34] Helton, J., Nie, J.: A semidefinite approach for truncated K-moment problem. Found. Compu. Math. 12(6), 851–881 (2012)
[35] Nie, J.: The A-truncated K-moment problem. Found. Compu. Math. 14(6), 1243–1276 (2014)
[36] Ross, S.: A First Course in Probability. Pearson (2010)
[37] Henrion, D., Lasserre, J.B., Löfberg, J.: GloptiPoly 3: moments, optimization and semidefinite programming. Optim. Methods Softw. 24(4–5), 761–779 (2009)
[38] Sturm, J.F.: Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optim. Methods Softw. 11(1–4), 625–653 (1999)
