OSX and Linux: 
This is a rudimentary Julia interface to the AMPL Solver Library (ASL). Installing on OSX and Linux should be easy using Homebrew and LinuxBrew:
Make sure you have the ASL:
brew tap homebrew/science
brew install asl
At the Julia prompt, clone this repository and build:
julia> Pkg.clone("https://github.com/optimizers/NLP.jl.git")
julia> Pkg.clone("https://github.com/dpo/ampl.jl.git")
julia> Pkg.build("ampl")In order for Julia to find the AMPL interface library, its location must
appear on your LD_LIBRARY_PATH:
export LD_LIBRARY_PATH=$(julia -E 'Pkg.dir()' | sed -e 's/"//g')/ampl/src:$LD_LIBRARY_PATH
Place the above in your ~/.bashrc to make it permanent.
julia> Pkg.test("ampl")For an introduction to the AMPL modeling language, see
- R. Fourer, D. M. Gay, and B. W. Kernighan, AMPL: A Mathematical Programming Language, Management Science 36, pp. 519-554, 1990.
- R. Fourer, D. M. Gay, and B. W. Kernighan, AMPL: A Modeling Language for Mathematical Programming, Duxbury Press / Brooks/Cole Publishing Company, 2003.
- D. Orban, The Lightning AMPL Tutorial. A Guide for Nonlinear Optimization Users, GERAD Technical Report G-2009-66, 2009.
Suppose you have an AMPL model represented by the model and data files mymodel.mod and mymodel.dat. Decode this model as a so-called nl file using
ampl -ogmymodel mymodel.mod mymodel.dat
For example:
julia> using ampl
julia> hs33 = AmplModel("hs033.nl")
Minimization problem hs033.nl
nvar = 3, ncon = 2 (0 linear)
julia> print(hs33)
Minimization problem hs033.nl
nvar = 3, ncon = 2 (0 linear)
lvar = 1x3 Array{Float64,2}:
0.0 0.0 0.0
uvar = 1x3 Array{Float64,2}:
Inf Inf 5.0
lcon = 1x2 Array{Float64,2}:
-Inf 4.0
ucon = 1x2 Array{Float64,2}:
0.0 Inf
x0 = 1x3 Array{Float64,2}:
0.0 0.0 3.0
y0 = 1x2 Array{Float64,2}:
-0.0 -0.0There is preliminary support for holding multiple models in memory simultaneously. This should be transparent to the user.
ampl.jl currently focuses on continuous problems written in the form
optimize f(x) subject to l ≤ x ≤ u, L ≤ c(x) ≤ U,
where f is the objective function, c is the (vector-valued) constraint function, l and u are vectors of lower and upper bounds on the variables, and L and U are vectors of lower and upper bounds on the general constraints.
AmplModel objects have the following attributes:
| Attribute | Type | Notes |
|---|---|---|
meta |
NLPModelMeta |
See NLP.jl |
__asl |
Ptr{Void} |
A private pointer to the internal ASL data structure |
The following table lists the methods associated to an AmplModel. See Hooking your Solver to AMPL for background.
| Method | Notes |
|---|---|
varscale(nlp, s) |
Scale the vector of variables by the vector s |
obj(nlp, x) |
Evaluate the objective function at x |
grad(nlp, x) |
Evaluate the objective function gradient at x |
lagscale(nlp, s) |
Set the scaling factor in the Lagrangian |
conscale(nlp, s) |
Scale the vector of constraints by the vector s |
cons(nlp, x) |
Evaluate the vector of constraints at x |
jth_con(nlp, x, j) |
Evaluate the j-th constraint at x |
jth_congrad(nlp, x, j) |
Evaluate the j-th constraint gradient at x |
jth_sparse_congrad(nlp, x, j) |
Evaluate the j-th constraint sparse gradient at x |
jac_coord(nlp, x) |
Evaluate the sparse Jacobian of the constraints at x in coordinate format |
jac(nlp, x) |
Evaluate the sparse Jacobian of the constraints at x |
hprod(nlp, x, v, y=y0, w=1) |
Evaluate the product of the Hessian of the Lagrangian at (x,y) with v using the objective weight w |
jth_hprod(nlp, x, v, j) |
Compute the product of the Hessian of the j-th constraint at x with v |
ghjvprod(nlp, x, g, v) |
Compute the vector of dot products (g, Hj*v) |
hess_coord(nlp, x, y=y0, w=1.) |
Evaluate the sparse Hessian of the Lagrangian at (x,y) using the objective weight w in coordinate format |
hess(nlp, x, y=y0, w=1.) |
Evaluate the sparse Hessian of the Lagrangian at (x,y) using the objective weight w |
write_sol(nlp, msg, x, y) |
Write primal and dual solutions to file |
- methods for LPs (sparse cost, sparse contraint matrix)
- methods to check optimality conditions.
This content is released under the MIT License.
