z <- 1.64

gamma <- as.matrix(c(-4.774226e-05,9.494081e-05,-1.611433e-05,  2.208759e-04, -5.217160e-05, 5.179586e-05, -1.730335e-04), nrow = 1, ncol = 7)

covariance <- matrix(c(5.493254e-04, 5.560772e-05, 5.240374e-05,  3.139825e-05,   1.047852e-04,   1.530925e-05, 6.106681e-05,
        5.560772e-05, 6.324495e-04, 5.903184e-05,  5.252309e-05,   1.378872e-04,   2.759473e-05, 6.330838e-05,
        5.240374e-05, 5.903184e-05, 4.304366e-04,  4.148924e-05,   8.047900e-05,   4.684000e-05, 5.132516e-05,
        3.139825e-05, 5.252309e-05, 4.148924e-05,  8.597828e-04,   2.564098e-05,  -1.242994e-05, 2.465488e-05,
        1.047852e-04, 1.378872e-04, 8.047900e-05,  2.564098e-05,   5.218737e-03,  -5.601413e-05, 4.621707e-05,
        1.530925e-05, 2.759473e-05, 4.684000e-05, -1.242994e-05,  -5.601413e-05,   4.736119e-04, 8.109542e-06,
        6.106681e-05, 6.330838e-05, 5.132516e-05,  2.465488e-05,   4.621707e-05,   8.109542e-06, 2.573109e-04), ncol = 7 , nrow = 7)


expected.return <- as.matrix(c(0.0011742096,  -0.0001458900,  -0.0016942349,   0.0017453739,   0.0013532639,   0.0003049205,  -0.0005496011), ncol = 7, nrow = 1 )

A <- matrix (1, nrow=ncol(covariance))
b <- 1

c <- 0
b <- c(b, rep(c,nrow=ncol(covariance)))

solve.QP(Dmat, dvec, Amat = A, bvec = b, meq = 2)