set.seed(123) nO <- 10L ## number of observations nA <- 100L ## number of assets mData <- array(rnorm(nO * nA, sd = 0.05), dim = c(nO, nA)) #Creating sample stock observations library("quadprog") aMat <- array(1, dim = c(1,nA)) bVec <- 1 zeros <- array(0, dim = c(nA,1)) solQP <- solve.QP(cov(mData), zeros, t(aMat), bVec, meq = 1) #Minimize optimization solQP$solution
I added the line that explicitly sets the seed to make the runs reproducible.
The model is a bit difficult to decipher in this R code, but it is a simplified traditional MV model:
| Mean-Variance Portfolio Optimization Model |
|---|
| \[\begin{align} \min\> & \color{darkred}x'\color{darkblue}Q\color{darkred}x - \color{darkblue}\lambda \bar{\color{darkblue}r}'\color{darkred}x\\ & \sum_i \color{darkred}x_i = 1 \\ & \color{darkred}x_i \ge 0\end{align}\] |
Instead of having the expected returns in the objective, we also see versions of this model with a linear constraint that puts a minimum value on the expected return of the portfolio.
In the example, we have \(\lambda=0\), so the objective only has a quadratic term to minimize the risk. We don't care about returns.
The covariance matrix is in theory positive-semi definite. We can see this from using the definition of the covariance [2]. However, due to floating-point inaccuracies, we can end up with a covariance matrix that is just slightly non-positive semi-definite. We can see this by inspecting the eigenvalues:
Most of the eigenvalues are basically zero with quite a few just a bit negative. A positive-definite matrix would have only positive values. A positive semi-definite (PSD) matrix would allow positive and zero eigenvalues but no negative ones.
Here we use our covariance matrix directly. Both CVXR and OSQP accept it, even though it has tiny negative numbers. However, the OSQP documentation warns that this may be a bit dangerous [3]:
Note that the data only has 10 observations but 100 instruments. This situation can often lead to some problems. Indeed when we run this, we see:
> solQP <- solve.QP(cov(mData), zeros, t(aMat), bVec, meq = 1) #Minimize optimization Error in solve.QP(cov(mData), zeros, t(aMat), bVec, meq = 1) : matrix D in quadratic function is not positive definite! >
As a side note: the data is just completely random, so we cannot expect useful solutions. However, at least, we should be able to produce some solutions.
Solution #1
> Q <- cov(mData) > eigen(Q)$values [1] 3.977635e-02 3.789523e-02 3.258164e-02 3.093782e-02 2.605389e-02 2.466463e-02 2.127899e-02 [8] 1.897506e-02 1.627900e-02 1.871945e-17 1.454315e-17 8.114384e-18 5.109196e-18 4.392427e-18 [15] 3.680766e-18 2.728485e-18 2.649302e-18 2.192073e-18 1.937634e-18 1.914487e-18 1.856811e-18 [22] 1.449321e-18 1.391434e-18 1.211216e-18 1.196721e-18 1.127119e-18 9.776146e-19 9.670197e-19 [29] 8.257462e-19 7.572554e-19 6.283522e-19 6.030820e-19 4.056541e-19 3.610128e-19 3.604800e-19 [36] 2.770106e-19 2.530797e-19 2.263314e-19 2.133854e-19 2.089700e-19 2.024130e-19 2.006560e-19 [43] 1.688435e-19 9.412675e-20 7.033666e-20 7.012531e-20 5.942729e-20 5.673501e-20 4.444257e-20 [50] 4.259787e-20 3.124233e-21 -2.017011e-20 -2.269857e-20 -3.034668e-20 -3.075493e-20 -3.262642e-20 [57] -4.345710e-20 -6.351674e-20 -7.347496e-20 -7.739535e-20 -8.065131e-20 -9.318415e-20 -1.004645e-19 [64] -1.048992e-19 -1.185271e-19 -1.315539e-19 -1.362383e-19 -1.376705e-19 -1.456396e-19 -1.697860e-19 [71] -1.700317e-19 -1.745068e-19 -1.794480e-19 -1.835296e-19 -1.960933e-19 -2.017457e-19 -2.332259e-19 [78] -2.377082e-19 -3.232830e-19 -3.468225e-19 -3.844707e-19 -4.310383e-19 -4.467493e-19 -4.540707e-19 [85] -4.977900e-19 -6.159103e-19 -6.338311e-19 -6.484161e-19 -6.489508e-19 -6.551306e-19 -6.664641e-19 [92] -7.276688e-19 -7.422977e-19 -7.927766e-19 -1.106209e-18 -1.707077e-18 -1.742870e-18 -2.465438e-18 [99] -1.859346e-17 -3.473638e-17
Most of the eigenvalues are basically zero with quite a few just a bit negative. A positive-definite matrix would have only positive values. A positive semi-definite (PSD) matrix would allow positive and zero eigenvalues but no negative ones.
There are algorithms to find a close-by positive-definite matrix. In R this is available as nearPD() in the Matrix package. Let's try this out:
> Q <- nearPD(cov(mData))$mat > solQP <- solve.QP(Q, zeros, t(aMat), bVec, meq = 1) > solQP$solution [1] 0.007861808 0.004649581 0.012677143 0.009135964 0.011926439 0.013624409 0.007916747 0.004307772 [9] 0.011808486 0.011085302 0.010229029 0.013080240 0.010085504 0.010998879 0.011099784 0.008520520 [17] 0.007451730 0.012424371 0.012677330 0.007013515 0.009070013 0.010422999 0.011041407 0.008768456 [25] 0.007594051 0.010845828 0.008091472 0.010219700 0.017100764 0.005583947 0.011981802 0.007366402 [33] 0.013770522 0.009063828 0.010744898 0.007836608 0.009580356 0.007065588 0.008219370 0.010592597 [41] 0.011775598 0.011112051 0.005555992 0.014596684 0.016019481 0.008870919 0.010940604 0.005662200 [49] 0.007056997 0.012274299 0.006510091 0.012764160 0.008258651 0.009945869 0.009891680 0.007318763 [57] 0.014960558 0.008255708 0.009655097 0.010054760 0.007309219 0.008761360 0.011616297 0.010300254 [65] 0.012043733 0.011963495 0.009991699 0.010353212 0.013896332 0.011619403 0.007160011 0.007202327 [73] 0.008530819 0.010918658 0.004754215 0.008877062 0.008526937 0.010251250 0.008190446 0.006754629 [81] 0.013209816 0.014849748 0.012213860 0.008888433 0.018188014 0.009756690 0.014786487 0.014893706 [89] 0.009737739 0.013256532 0.009990737 -0.001155889 0.009264992 0.007733719 0.014505131 0.011040198 [97] 0.012859850 0.008987104 0.008458713 0.004497735
Much better. When printing the eigenvalues of the new Q matrix, we see:
> eigen(Q)$values
[1] 3.977635e-02 3.789522e-02 3.258164e-02 3.093782e-02 2.605389e-02 2.466462e-02 2.127899e-02 1.897506e-02
[9] 1.627900e-02 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[17] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[25] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[33] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[41] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[49] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[57] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[65] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10
[73] 3.977635e-10 3.977635e-10 3.977635e-10 3.977635e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10
[81] 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10
[89] 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10 3.977634e-10
[97] 3.977634e-10 3.977634e-10 3.977633e-10 3.977633e-10
All small (and negative) eigenvalues are now mapped to some tolerance. This means the Q matrix is now numerically positive definite.
Solution #2
A simple DIY approach would be to fix the Q matrix by adding a small number to the diagonal. The following code shows a small perturbation is enough:
> Q <- cov(mData) + diag(1e-6,nA)
Some solvers do something like this automatically.
Fixing the model
Let's first fix our model a bit:
- We want to pay attention to returns. So let's add a constraint that the expected portfolio returns are at least 0.035. This also has the effect that just a few assets will be in our solution. Which makes them easier to compare. The constraint can be stated as \[\sum_i \bar{r}_i \ge 0.035\] where \[\bar{r}_i = \frac{\sum_t r_{t,i}}{T}\] are the mean returns. Here \(T\) indicates the number of observations, and \(r_{t,i}\) are the returns.
- We also want to explicitly state that variables are non-negative: \(x_i \ge 0\). This is called "no short-selling",
The portfolio model becomes:
| Portfolio Model algebraic formulation | matrix notation |
|---|---|
| \[\begin{align} \min & \sum_{i,j} \color{darkblue}q_{i,j} \color{darkred}x_i \color{darkred}x_j \\ & \sum_i \color{darkred}x_i = 1 \\ & \sum_i \bar{\color{darkblue}r}_i \color{darkred}x_i \ge 0.035\\ & \color{darkred}x_i \ge 0\end{align}\] | \[\begin{align} \min \>& \color{darkred}x' \color{darkblue}Q \color{darkred}x \\ & \color{darkblue}e'\color{darkred}x = 1 \\ & \bar{\color{darkblue}r}' \color{darkred}x \ge 0.035\\ & \color{darkred}x \ge 0\end{align}\] |
QuadProg does not have facilities for bounds on the variables so we need to form explicit constraints. Its input-model looks like: \[\begin{align} \min \> & -d'b + \frac{1}{2} b'Db \\ & A'b\ge b_0\end{align}\] where the first meq constraints are equalities. The factor 0.5 is a relic from quadratic optimization (it makes the gradient a bit easier). If the model includes linear objective coefficients, it is important to take this 0.5 factor into account. Our model has only quadratic objective coefficients, so we don't care in this case. All in all, this is often a bit of an inconvenient input format.
R trick. In the examples below we want to display a sparse vector. A simple trick using names can help here.
> v <- c(0,0,3,0,0,0,1,0,0,0,2,0,0,0,0) > v [1] 0 0 3 0 0 0 1 0 0 0 2 0 0 0 0 > names(v) <- paste("v",1:length(v),sep="") > v[v>0] v3 v7 v11 3 1 2
The R code for our optimization model can look like:
> Q <- nearPD(cov(mData))$mat > aMat <- rbind( + c(rep(1,nA)), # sum(x)=1 + colMeans(mData), # sum(mu(i)*x(i))>=0.035 + diag(1,nA)) # for bounds x(i)>=0 > bVec <- c(1, + 0.035, + rep(0,nA)) > solQP <- solve.QP(Q, zeros, t(aMat), bVec, meq = 1) > xsol <- solQP$solution > names(xsol) = paste("x",1:100,sep="") > xsol[xsol>1e-4] x30 x75 x88 0.1465990 0.5314489 0.3219522
Another issue with QuadProg is that requires a strictly positive definite matrix. This also means it does not allow linear variables: all variables need appear to quadratically in the objective (and without all zero quadratic coefficients). After all, if the quadratic coefficient matrix would look like \[D = \left[\begin{array}{c|c}Q & 0 \\ \hline 0 & 0 \end{array} \right]\] we would end up with a matrix that is not strictly positive-definite.
Often, for these types of models, CVXR is a more agreeable tool. Here is the same model in CVXR, and solved with the OSQP QP solver:
> library(CVXR) > x <- Variable(nA) > w <- Variable(nO) > prob <- Problem( + Minimize(quad_form(x,cov(mData))), + list(sum(x) == 1, + t(colMeans(mData)) %*% x >= 0.035, + x >= 0) + ) > result <- solve(prob, verbose=T) ----------------------------------------------------------------- OSQP v0.6.0 - Operator Splitting QP Solver (c) Bartolomeo Stellato, Goran Banjac University of Oxford - Stanford University 2019 ----------------------------------------------------------------- problem: variables n = 100, constraints m = 102 nnz(P) + nnz(A) = 5350 settings: linear system solver = qdldl, eps_abs = 1.0e-05, eps_rel = 1.0e-05, eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04, rho = 1.00e-01 (adaptive), sigma = 1.00e-06, alpha = 1.60, max_iter = 10000 check_termination: on (interval 25), scaling: on, scaled_termination: off warm start: on, polish: on, time_limit: off iter objective pri res dua res rho time 1 0.0000e+00 1.00e+00 1.00e+02 1.00e-01 2.88e-03s 200 8.7535e-04 5.96e-04 1.36e-06 1.75e-02 9.38e-03s 400 9.2707e-04 5.09e-04 1.16e-06 1.75e-02 1.41e-02s 600 9.7703e-04 4.36e-04 9.94e-07 1.75e-02 1.99e-02s 800 1.0239e-03 3.72e-04 8.50e-07 1.75e-02 2.58e-02s 1000 1.0671e-03 3.18e-04 7.27e-07 1.75e-02 3.11e-02s 1200 1.1062e-03 2.72e-04 6.21e-07 1.75e-02 3.76e-02s 1400 1.1413e-03 2.33e-04 5.31e-07 1.75e-02 4.35e-02s 1600 1.1726e-03 1.99e-04 4.54e-07 1.75e-02 4.90e-02s 1800 1.2001e-03 1.70e-04 3.88e-07 1.75e-02 5.51e-02s 2000 1.2243e-03 1.45e-04 3.32e-07 1.75e-02 6.20e-02s 2200 1.2455e-03 1.24e-04 2.84e-07 1.75e-02 6.81e-02s 2400 1.2639e-03 1.06e-04 2.43e-07 1.75e-02 7.33e-02s 2600 1.2799e-03 9.09e-05 2.07e-07 1.75e-02 7.83e-02s 2800 1.2938e-03 7.77e-05 1.77e-07 1.75e-02 8.51e-02s 3000 1.3058e-03 6.65e-05 1.52e-07 1.75e-02 9.20e-02s 3200 1.3161e-03 5.68e-05 1.30e-07 1.75e-02 1.00e-01s 3400 1.3251e-03 4.86e-05 1.11e-07 1.75e-02 1.08e-01s 3600 1.3327e-03 4.15e-05 9.48e-08 1.75e-02 1.21e-01s 3800 1.3394e-03 3.55e-05 8.11e-08 1.75e-02 1.28e-01s 4000 1.3450e-03 3.04e-05 6.93e-08 1.75e-02 1.35e-01s 4200 1.3499e-03 2.60e-05 5.92e-08 1.75e-02 1.43e-01s 4400 1.3541e-03 2.22e-05 5.07e-08 1.75e-02 1.50e-01s 4550 1.3568e-03 1.97e-05 4.50e-08 1.75e-02 1.56e-01s plsh 1.3790e-03 3.33e-16 1.21e-15 --------- 1.58e-01s status: solved solution polish: successful number of iterations: 4550 optimal objective: 0.0014 run time: 1.58e-01s optimal rho estimate: 2.58e-02 > xsol <- result$getValue(x) > names(xsol) = paste("x",1:100,sep="") > xsol[xsol>1e-4] x30 x75 x88 0.1465989 0.5314489 0.3219522 >
Here we use our covariance matrix directly. Both CVXR and OSQP accept it, even though it has tiny negative numbers. However, the OSQP documentation warns that this may be a bit dangerous [3]:
We recommend the user to check the convexity of their problem before passing it to OSQP! If the user passes a non-convex problem we do not assure the solver will be able to detect it.
OSQP will try to detect non-convex problems by checking if the residuals diverge or if there are any issues in the initial factorization (if a direct method is used). It will detect non-convex problems when one or more of the eigenvalues of
Pare “clearly” negative, i.e., whenP + sigma * Iis not positive semidefinite. However, it might fail to detect non-convexity whenPhas slightly negative eigenvalues, i.e., whenP + sigma * Iis positive semidefinite andPis not.
Notice that CVXR will complain if the Q matrix is really non-positive semi-definite.
Solution #3
In [2] a different model is proposed for these situations where we have more instruments than observations.
| Means-adjusted-returns Portfolio Optimization Model |
|---|
| \[\begin{align} \min\> & \sum_t \color{darkred}w_t^2 \\ &\color{darkred}w_t = \sum_i (\color{darkblue}r_{t,i} - \bar{\color{darkblue}r}_i)\color{darkred}x_i \\ & \sum_i \color{darkred}x_i = 1 \\ & \sum_i \bar{\color{darkblue}r}_i \color{darkred}x_i \ge 0.035\\ & \color{darkred}x_i \ge 0\end{align}\] |
Some of the advantages of this model are:
- The quadratic objective is benign. It is sparse and safely positive definite.
- The data is smaller than a full-blown covariance matrix given that the number of observations is smaller than the number of instruments.
- The number of quadratic variables is smaller (again, because \(T<N\)).
This model is easily implemented in CVXR:
> library(CVXR) > x <- Variable(nA) > w <- Variable(nO) > adjRet <- mData - matrix(colMeans(mData),nrow=nO,ncol=nA,byrow=T) > prob <- Problem( + Minimize(sum_squares(w)), + list(w == adjRet %*% x, + sum(x)==1, + t(colMeans(mData)) %*% x >= 0.035, + x >= 0) + ) > result <- solve(prob, verbose=T) ----------------------------------------------------------------- OSQP v0.6.0 - Operator Splitting QP Solver (c) Bartolomeo Stellato, Goran Banjac University of Oxford - Stanford University 2019 ----------------------------------------------------------------- problem: variables n = 110, constraints m = 112 nnz(P) + nnz(A) = 1320 settings: linear system solver = qdldl, eps_abs = 1.0e-05, eps_rel = 1.0e-05, eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04, rho = 1.00e-01 (adaptive), sigma = 1.00e-06, alpha = 1.60, max_iter = 10000 check_termination: on (interval 25), scaling: on, scaled_termination: off warm start: on, polish: on, time_limit: off iter objective pri res dua res rho time 1 0.0000e+00 1.00e+00 1.00e+02 1.00e-01 1.15e-03s 200 5.3515e-03 1.49e-03 4.04e-05 1.00e-01 4.33e-03s 400 7.0787e-03 7.87e-04 6.50e-06 1.00e-01 8.41e-03s 600 7.3521e-03 7.13e-04 5.90e-06 1.00e-01 1.23e-02s 800 7.6413e-03 6.45e-04 5.34e-06 1.00e-01 1.61e-02s 1000 7.9373e-03 5.84e-04 4.84e-06 1.00e-01 1.93e-02s 1200 8.2334e-03 5.29e-04 4.38e-06 1.00e-01 2.27e-02s 1400 8.5244e-03 4.79e-04 3.96e-06 1.00e-01 2.64e-02s 1600 8.8067e-03 4.33e-04 3.59e-06 1.00e-01 3.00e-02s 1800 9.0778e-03 3.92e-04 3.25e-06 1.00e-01 3.34e-02s 2000 9.3358e-03 3.55e-04 2.94e-06 1.00e-01 3.69e-02s 2200 9.5798e-03 3.22e-04 2.66e-06 1.00e-01 4.05e-02s 2400 9.8091e-03 2.91e-04 2.41e-06 1.00e-01 4.43e-02s 2600 1.0024e-02 2.64e-04 2.18e-06 1.00e-01 4.81e-02s 2800 1.0224e-02 2.39e-04 1.98e-06 1.00e-01 5.12e-02s 3000 1.0410e-02 2.16e-04 1.79e-06 1.00e-01 5.48e-02s 3200 1.0582e-02 1.96e-04 1.62e-06 1.00e-01 5.84e-02s 3400 1.0741e-02 1.77e-04 1.47e-06 1.00e-01 6.19e-02s 3600 1.0887e-02 1.60e-04 1.33e-06 1.00e-01 6.55e-02s 3800 1.1022e-02 1.45e-04 1.20e-06 1.00e-01 6.90e-02s 4000 1.1145e-02 1.31e-04 1.09e-06 1.00e-01 7.53e-02s 4200 1.1259e-02 1.19e-04 9.85e-07 1.00e-01 8.18e-02s 4400 1.1362e-02 1.08e-04 8.92e-07 1.00e-01 8.95e-02s 4600 1.1457e-02 9.75e-05 8.07e-07 1.00e-01 9.44e-02s 4800 1.1544e-02 8.83e-05 7.31e-07 1.00e-01 1.04e-01s 5000 1.1623e-02 7.99e-05 6.62e-07 1.00e-01 1.09e-01s 5200 1.1695e-02 7.24e-05 5.99e-07 1.00e-01 1.13e-01s 5400 1.1761e-02 6.55e-05 5.42e-07 1.00e-01 1.20e-01s 5600 1.1821e-02 5.93e-05 4.91e-07 1.00e-01 1.26e-01s 5800 1.1876e-02 5.37e-05 4.45e-07 1.00e-01 1.31e-01s 6000 1.1925e-02 4.86e-05 4.02e-07 1.00e-01 1.37e-01s 6200 1.1970e-02 4.40e-05 3.64e-07 1.00e-01 1.45e-01s 6400 1.2011e-02 3.98e-05 3.30e-07 1.00e-01 1.58e-01s 6600 1.2049e-02 3.61e-05 2.99e-07 1.00e-01 1.72e-01s 6800 1.2082e-02 3.27e-05 2.70e-07 1.00e-01 1.81e-01s 7000 1.2113e-02 2.96e-05 2.45e-07 1.00e-01 1.90e-01s 7200 1.2141e-02 2.68e-05 2.22e-07 1.00e-01 1.98e-01s 7400 1.2166e-02 2.42e-05 2.01e-07 1.00e-01 2.01e-01s 7600 1.2189e-02 2.19e-05 1.82e-07 1.00e-01 2.05e-01s 7800 1.2210e-02 1.99e-05 1.64e-07 1.00e-01 2.08e-01s plsh 1.2411e-02 6.66e-16 1.28e-15 --------- 2.09e-01s status: solved solution polish: successful number of iterations: 7800 optimal objective: 0.0124 run time: 2.09e-01s optimal rho estimate: 3.27e-01 > xsol <- result$getValue(x) > names(xsol) = paste("x",1:100,sep="") > xsol[xsol>1e-4] x30 x75 x88 0.1465989 0.5314489 0.3219522
Using QuadProg this is a much more difficult exercise. The quadratic coefficient matrix \(D\) will look like: \[D = \left[\begin{array}{c|c}Q & 0 \\ \hline 0 & \varepsilon I \end{array} \right]\] The R code can look like:
> Q <- nearPD(cov(mData))$mat > D <- rbind( + cbind(Q,matrix(0,nrow=nA,ncol=nO)), + cbind(matrix(0,nrow=nO,ncol=nA),diag(1e-6,nO)) + ) > aMat <- rbind( + cbind(adjRet,diag(-1,nO)), # sum(i,adjRet(t,i)*x(i))-w(t)=0 + c(rep(1,nA),rep(0,nO)), # sum(x)=1 + c(colMeans(mData),rep(0,nO)), # sum(mu(i)*x(i))>=0.035 + cbind(diag(1,nA),matrix(0,nrow=nA,ncol=nO)) # for bounds x(i)>=0 + ) > bVec <- c(rep(0,nO), + 1, + 0.035, + rep(0,nA)) > zeros <- rep(0,nA+nO) > solQP <- solve.QP(D, zeros, t(aMat), bVec, meq = nO+1) > xsol <- solQP$solution[1:nA] > names(xsol) = paste("x",1:100,sep="") > xsol[xsol>1e-4] x30 x75 x88 0.1465990 0.5314489 0.3219522
Conclusion
QuadProg is a well-known QP solver for R. Unfortunately it has some drawbacks:
- It is not very suited for more complex models: the matrix oriented input format is just too cumbersome.
- The quadratic coefficient matrix must be strictly positive definite.
- All variables are quadratic, linear variables are not available.
For quick experimentation with these types of models, CVXR is likely a better environment. This exercise showed:
- The models are more readable, assuming you are familiar with matrix notation.
- We can formulate models with linear variables without a problem.
- Models with a quadratic coefficient matrix that are borderline positive semi-definite are accepted by both CVXR and the solver I used: OSQP.
Two useful R tricks are discussed:
- nearPD(A) to find a positive-definite matrix close to A.
- Printing a sparse vector using names.
References
- R minimum variance portfolio: solve non invertible matrix, https://stackoverflow.com/questions/64067591/r-minimum-variance-portfolio-solve-non-invertible-matrix
- Covariance Matrix not positive definite in portfolio models, https://yetanothermathprogrammingconsultant.blogspot.com/2018/04/covariance-matrix-not-positive-definite.html
- OSQP, Status values and errors, https://osqp.org/docs/interfaces/status_values.html
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