Well, but in the upgrading guide there is no replacement for GradientNumber mentioned.
Any idea? Uwe On Monday, August 8, 2016 at 7:14:45 PM UTC+2, Miles Lubin wrote: > > ForwardDiff 0.2 introduced some breaking changes, you will need to update > your code (GradientNumber is no longer defined). See the upgrading guide > <http://www.juliadiff.org/ForwardDiff.jl/upgrade.html>. > > On Monday, August 8, 2016 at 11:10:50 AM UTC-6, Uwe Fechner wrote: >> >> Hello, >> I updated, and now I get the following error: >> julia> include("Plotting.jl") >> INFO: Recompiling stale cache file /home/ufechner/.julia/lib/v0.4/JuMP.ji >> for module JuMP. >> INFO: Recompiling stale cache file >> /home/ufechner/.julia/lib/v0.4/ReverseDiffSparse.ji for module >> ReverseDiffSparse. >> INFO: Recompiling stale cache file >> /home/ufechner/.julia/lib/v0.4/ForwardDiff.ji for module ForwardDiff. >> INFO: Recompiling stale cache file /home/ufechner/.julia/lib/v0.4/HDF5.ji >> for module HDF5. >> ERROR: LoadError: LoadError: LoadError: LoadError: UndefVarError: >> GradientNumber not defined >> while loading /home/ufechner/00PythonSoftware/FastSim/src/Projects.jl, in >> expression starting on line 433 >> while loading /home/ufechner/00PythonSoftware/FastSim/src/Model.jl, in >> expression starting on line 19 >> while loading /home/ufechner/00PythonSoftware/FastSim/src/Optimizer.jl, >> in expression starting on line 13 >> while loading /home/ufechner/00PythonSoftware/FastSim/src/Plotting.jl, in >> expression starting on line 22 >> >> The code, that fails is the following: >> """ >> Helper function to convert the value of an optimization results, but also >> simple real values. >> """ >> my_value(value::ForwardDiff.GradientNumber) = ForwardDiff.value(value) >> my_value(value::Real) = value >> my_value(val_vector::Vector) = [my_value(value) for value in val_vector] >> >> Any idea how to fix this? >> >> Uwe >> >> On Monday, August 8, 2016 at 4:57:16 PM UTC+2, Miles Lubin wrote: >>> >>> The JuMP team is happy to announce the release of JuMP 0.14. The release >>> should clear most, if not all, deprecation warnings on Julia 0.5 and is >>> compatible with ForwardDiff 0.2. The full release notes are here >>> <https://github.com/JuliaOpt/JuMP.jl/blob/master/NEWS.md#version-0140-august-7-2016>, >>> >>> and I'd just like to highlight a few points: >>> >>> - *All JuMP users read this*: As previously announced >>> <https://groups.google.com/d/msg/julia-opt/vUK1NHEHqfk/WD-6lSbMCAAJ>, we >>> will be deprecating the sum{}, prod{}, and norm{} syntax in favor of using >>> Julia 0.5's new syntax for generator statements, e.g., sum(x[i] for i >>> in 1:N) instead of sum{x[i], i in 1:N}. In this release, the new syntax >>> is available for testing if using Julia 0.5. No deprecation warnings are >>> printed yet. In JuMP 0.15, which will drop support for Julia 0.4, we will >>> begin printing deprecation warnings for the old syntax. >>> >>> - *Advanced JuMP users read this*: We have introduced a new syntax for >>> "anonymous" objects, which means that when declaring an optimization >>> variable, constraint, expression, or parameter, you may omit the name of >>> the object within the macro. The macro will instead return the object >>> itself which you can assign to a variable if you'd like. Example: >>> >>> # instead of @variable(m, l[i] <= x[i=1:N] <= u[i]): >>> x = @variable(m, [i=1:N], lowerbound=l[i], upperbound=u[i]) >>> >>> This syntax should be comfortable for advanced use cases of JuMP (e.g., >>> within a library) and should obviate some confusions about JuMP's variable >>> scoping rules. >>> >>> - We also have a new input form for nonlinear expressions that has the >>> potential to extend JuMP's scope as an AD tool. Previously all nonlinear >>> expressions needed to be input via macros, which isn't convenient if the >>> expression is generated programmatically. You can now set nonlinear >>> objectives and add nonlinear constraints by providing a Julia Expr >>> object directly with JuMP variables spliced in. This means that you can now >>> generate expressions via symbolic manipulation and add them directly to a >>> JuMP model. See the example in the documentation >>> <http://www.juliaopt.org/JuMP.jl/0.14/nlp.html#raw-expression-input>. >>> >>> Finally, I'd like to thank Joaquim Dias Garcia, Oscar Dowson, Mehdi >>> Madani, and Jarrett Revels for contributions to this release which are >>> cited in the release notes. >>> >>> Miles, Iain, and Joey >>> >>> >>>
