Mathematica NMinimize[] vs Julia with the CPLEX Solver

I setup a simple Convex Optimization problem ans pointed Mathematica and Julia at it.  I am using the CPLEX commercial solver from IBM with Julia.  The problem is documented in this book.

Image result for convex optimization Julia

Here is the Julia code:

using JuMP, CPLEX
m = Model(solver = CplexSolver())

@variable(m, x[1:2])
@objective(m, Min, (x[1]-3)^2 + (x[2]-4)^2)

@constraint(m, (x[1]-1)^2 + (x[2]+1)^2 <=1)
println("Problem As Interpreted by Model")
print(m)
status = solve(m)

println("*** Objective value: ", getobjectivevalue(m))
println("*** Optimal solution: ", getvalue(x))
println(" y = ", getvalue(y))

Here is the output of the Julia code above:

Problem As Interpreted by Model
Min x[1]² + x[2]² - 6 x[1] - 8 x[2] + 25
Subject to
 x[1]² + x[2]² - 2 x[1] + 2 x[2] + 1 <= 0
 x[i] free for all i in {1,2}
Tried aggregator 1 time.
Aggregator did 1 substitutions.
Reduced QCP has 6 rows, 8 columns, and 12 nonzeros.
Reduced QCP has 2 quadratic constraints.
Presolve time = 0.00 sec. (0.00 ticks)
Parallel mode: using up to 8 threads for barrier.
Number of nonzeros in lower triangle of A*A' = 11
Using Approximate Minimum Degree ordering
Total time for automatic ordering = 0.00 sec. (0.00 ticks)
Summary statistics for Cholesky factor:
  Threads                   = 8
  Rows in Factor            = 6
  Integer space required    = 6
  Total non-zeros in factor = 21
  Total FP ops to factor    = 91
 Itn      Primal Obj        Dual Obj  Prim Inf Upper Inf  Dual Inf Inf Ratio
   0  1.8284271e+000 -1.0000000e+000 1.97e+000 0.00e+000 1.70e+001 1.00e+000
   1 -7.6831919e+000 -5.9358407e+000 1.97e+000 0.00e+000 1.70e+001 2.46e-001
   2 -4.5996777e+000 -4.2164027e+000 1.19e+000 0.00e+000 1.03e+001 5.97e-001
   3 -6.1584389e+000 -6.1095871e+000 6.63e-001 0.00e+000 5.73e+000 4.29e+000
   4 -5.8155562e+000 -5.8268221e+000 9.46e-002 0.00e+000 8.19e-001 2.00e+001
   5 -5.8335646e+000 -5.8363252e+000 3.15e-002 0.00e+000 2.72e-001 5.24e+001
   6 -5.8029020e+000 -5.8018579e+000 1.12e-002 0.00e+000 9.70e-002 4.91e+001
   7 -5.7931959e+000 -5.7920153e+000 9.92e-003 0.00e+000 8.58e-002 1.07e+002
   8 -5.7725419e+000 -5.7725795e+000 4.17e-003 0.00e+000 3.61e-002 8.95e+002
   9 -5.7710135e+000 -5.7709267e+000 5.93e-004 0.00e+000 5.13e-003 2.69e+003
  10 -5.7703538e+000 -5.7703509e+000 1.46e-004 0.00e+000 1.26e-003 8.58e+004
  11 -5.7703297e+000 -5.7703297e+000 4.47e-006 0.00e+000 3.87e-005 1.32e+007
  12 -5.7703296e+000 -5.7703296e+000 2.89e-008 0.00e+000 2.50e-007 8.44e+007
*** Objective value: 19.229670381903375
*** Optimal solution: [1.37144,-0.0715412]
 y = 0.2

Mathematica solves the problem very quietly but gets the wrong answer with NMimize[].

Here is the Mathematica code and the output:

(* Convex Optimization Problem *)
objectiveFunc = (x - 3)^2 + (y - 4)^2;
constraintFunc = (x - 1)^2  + (y - 4)^2 <= 1
v = NMinimize[{objectiveFunc, constraintFunc}, {x, y}]
{1., {x -> 2., y -> 4.}}

That is not close to being correct.  But I have heard that NMinimize[] does not do well with Convex optimization problems.  I'm trying the Gurobi solver next if I can get it to cooperate with Julia.