Better error message for invalid coefficients

The code below causes an error. For some reason, the way the array dx is constructed causes a problem with:
log_x[i] = piecewiselinear(m,x[i],dx[i],log,method=method)

julia monroe4.jl
Capacity available on '10.60.129.38' - connecting...
Established 256-bit AES encrypted connection
Method: Logarithmic
ERROR: LoadError: Invalid coefficient -Inf on variable λ_1[1]
Stacktrace:
[1] assert_isfinite(::JuMP.GenericAffExpr{Float64,JuMP.Variable}) at /Users/a598124/.julia/v0.6/JuMP/src/affexpr.jl:95
[2] prepConstrMatrix(::JuMP.Model) at /Users/a598124/.julia/v0.6/JuMP/src/solvers.jl:554
[3] #build#119(::Bool, ::Bool, ::JuMP.ProblemTraits, ::Function, ::JuMP.Model) at /Users/a598124/.julia/v0.6/JuMP/src/solvers.jl:358
[4] (::JuMP.#kw##build)(::Array{Any,1}, ::JuMP.#build, ::JuMP.Model) at ./:0
[5] #solve#116(::Bool, ::Bool, ::Bool, ::Array{Any,1}, ::Function, ::JuMP.Model) at /Users/a598124/.julia/v0.6/JuMP/src/solvers.jl:168
[6] macro expansion at /Users/a598124/juliaCode/boxPacking1/monroe4.jl:71 [inlined]
[7] anonymous at ./:?
[8] include_from_node1(::String) at ./loading.jl:576
[9] include(::String) at ./sysimg.jl:14
[10] process_options(::Base.JLOptions) at ./client.jl:305
[11] _start() at ./client.jl:371
while loading /Users/a598124/juliaCode/boxPacking1/monroe4.jl, in expression starting on line 41

using PiecewiseLinearOpt
using JuMP, Cbc, Gurobi, CPLEX

# Minimize volume = x*y*z by minimizing log(x*y*z) = log(x)+log(y)+log(z) through piecewise univariate linearization of log(x), log(y), and log(z).

#const solver = CbcSolver()
env = Gurobi.Env()
const solver = GurobiSolver(env,Threads=4)
#const solver = CplexSolver();
#methods_1D = (:CC,:MC,:Logarithmic,:LogarithmicIB,:ZigZag,:ZigZagInteger,:SOS2,:GeneralizedCelaya,:SymmetricCelaya,:Incremental,:DisaggLogarithmic)
#methods_1D = (:CC,:MC,:Logarithmic,:ZigZag,:ZigZagInteger,:SOS2,:GeneralizedCelaya,:SymmetricCelaya,:Incremental)
methods_1D = (:Logarithmic,:ZigZag,:ZigZagInteger)
#methods_1D = (:SOS2,:GeneralizedCelaya,:SymmetricCelaya,:Incremental)

dx2 = Array{Array{Float64}}(3)
dx2[1] = linspace(5,2π,8)
dx2[2] = linspace(4,2π,7)
dx2[3] = linspace(3,2π,9)

lb = Array{Float64}(3)
lb[1] = 0
lb[2] = 0
lb[3] = 0
ub = Array{Float64}(3)
ub[1] = 20
ub[2] = 20
ub[3] = 20

num_seg = Array{UInt8}(3)
num_seg[1] = 32
num_seg[2] = 32
num_seg[3] = 32

dx = Array{Array{Float64}}(3)
for i=1:3
dx[i] = linspace(lb[i],ub[i],num_seg[i])
#dx[i] = linspace(lb[i],9,num_seg[i])
#dx[i] = linspace(3,2π,9)
end

for method in methods_1D
println("Method: $method")

n = 4 # number of cartons
m = Model(solver=solver)

@variable(m,x[1:3]) # dimensions of minimum volume box containing all the cartons
setlowerbound(x[1],0)
setupperbound(x[1],20)
setlowerbound(x[2],0)
setupperbound(x[2],20)
setlowerbound(x[3],0)
setupperbound(x[3],20)
#dx = Array{Array{Float64}}(3)
#for i=1:3
#dx[i] = linspace(getlowerbound(x[i]),getupperbound(x[i]),num_seg[i])
#dx[i] = linspace(lb[i],ub[i],num_seg[i])
#dx[i] = linspace(0,2π,9)
#end

@variable(m,X[1:3,1:n]) # coordinates of left-back-bottom corner of each carton
@variable(m,l[1:3,1:3,1:n],Bin) # binary variables encoding the orientation of each carton
@variable(m,a[1:6,i=1:n,(i+1):n],Bin) # binary variables enforcing non-overlapping of pairs of cartons
@variable(m,log_x[1:3]) # piecewise linear approximation of log(x)
for i=1:3
#log_x[i] = piecewiselinear(m,x[i],dx2[i],log,method=method)
log_x[i] = piecewiselinear(m,x[i],dx[i],log,method=method)
end

@objective(m,Min,sum(log_x))
solve(m) == :Optimal

println("volume = x*y*z = ",prod(getvalue(x)))
println("[x,y,z] = ",getvalue(x))
end