dvec = matrix(colMeans(returns_full),ncol = 1)
Dmat = cov(returns_full)
A.Constraint1 <- matrix(c(1,1,1,1,1,1), ncol=1)
A.Constraint2 = matrix(c(1,1,1,1,1,1), ncol=1)
Amat <- cbind(A.Equality1,A.Equality2, dvec)
bvec <- c(-lev_ub,lev_lb,target_return)
qp <- solve.QP(Dmat, dvec, Amat, bvec, meq=0)

target_return <-  0.0002
num_assets <- 6
dvec <- rep(0,num_assets)                        #  dvec = 0 when minimizing only the variance
Dmat <- cov(return_full)
Amat <- matrix(rep(1, num_assets), ncol=num_assets)      #  budget constraint: sum of wts = 1
Amat <- rbind(Amat, c(.3, .7, -.3, 1, 1.1, 1.3) )        #  beta constraint: sum of wt*beta = 0
Amat <- rbind(Amat, colMeans(return_full) )              #  portfolio return = target_return
# Amat <- rbind(Amat, diag(1,nrow=num_assets, ncol=num_assets) )    #  would be used for long-only solutions: wts >= 0
Amat <- t(Amat)
bvec <- c(budget = 1, beta = 0, return = target_return )         # rhs of constraint eqns
# bvec <- c(bvec, rep(0,num_assets) )       # would be used with long-only constraint
qp <- solve.QP(Dmat, dvec, Amat, bvec, meq=3)