diff --git a/Project.toml b/Project.toml
index adffdb9..99d9e7d 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "Vecchia"
 uuid = "8d73829f-f4b0-474a-9580-cecc8e084068"
 authors = ["Chris Geoga <christopher.geoga@rutgers.edu>"]
-version = "0.9.11"
+version = "0.9.12"
 
 [deps]
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
diff --git a/example/example_estimate.jl b/example/example_estimate.jl
index 9734e18..6bef9d7 100644
--- a/example/example_estimate.jl
+++ b/example/example_estimate.jl
@@ -15,5 +15,5 @@ const cfg = Vecchia.kdtreeconfig(sim, # your simulated data, a Matrix{Float64}.
 # Now just compute the estimator and let autodiff and Ipopt take care of the rest!
 # Note that you can provide kwargs here for the optimizer. But if you're
 # providing your own optimizer you're probably customizing more than that anyway.
-#const estimator = vecchia_estimate(cfg, init[1:3], warn_box=false) 
+const estimator = vecchia_estimate(cfg, init[1:3], warn_box=false) 
 
diff --git a/src/sqp.jl b/src/sqp.jl
index 2d3b9b2..5ddddc5 100644
--- a/src/sqp.jl
+++ b/src/sqp.jl
@@ -52,7 +52,7 @@ end
 # cheap by comparison. So it is my hope/expectation, which has been sort of born
 # out through my own ad-hoc testing, that it can save at least a few iterations
 # total to do this exactly.
-function solve_qp(xk, gk, hk::Symmetric, delta, 
+function solve_qp(xk, gk, hk::Hermitian, delta, 
                   box_lower, box_upper, iter)
   try
     n   = length(xk)
@@ -98,7 +98,7 @@ function sqptr_optimize(f, init;
     catch er
       return failureresult(:FGH_ERROR, x0, j, er)
     end
-    mk  = LocalQuadraticApprox(fk, gk, Symmetric(hk))
+    mk  = LocalQuadraticApprox(fk, gk, Hermitian(hk))
     rho = 0.0
     while rho < args[:eta]
       vrb && print(".")