This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia.
If you use NLPModels.jl in your work, please cite using the format given in CITATION.bib.
Optimization problems are represented by an instance of (a subtype of)
AbstractNLPModel. Such instances are composed of
NLPModelMeta, which provides information about the problem, including the number of variables, constraints, bounds on the variables, etc.
See the documentation for details on the models and the API.
pkg> add NLPModels
This package provides no models, although it allows the definition of manually written models.
Check the list of packages that define models in this page of the docs
model is an instance of an appropriate subtype of
AbstractNLPModel, the following methods are normally defined:
obj(model, x): evaluate f(x), the objective at
cons(model x): evaluate c(x), the vector of general constraints at
The following methods are defined if first-order derivatives are available:
grad(model, x): evaluate ∇f(x), the objective gradient at
jac(model, x): evaluate J(x), the Jacobian of c at
xas a sparse matrix
If Jacobian-vector products can be computed more efficiently than by evaluating the Jacobian explicitly, the following methods may be implemented:
jprod(model, x, v): evaluate the result of the matrix-vector product J(x)⋅v
jtprod(model, x, u): evaluate the result of the matrix-vector product J(x)ᵀ⋅u
The following method is defined if second-order derivatives are available:
hess(model, x, y): evaluate ∇²L(x,y), the Hessian of the Lagrangian at
If Hessian-vector products can be computed more efficiently than by evaluating the Hessian explicitly, the following method may be implemented:
hprod(model, x, v, y): evaluate the result of the matrix-vector product ∇²L(x,y)⋅v
Several in-place variants of the methods above may also be implemented.
The complete list of methods that an interface may implement can be found in the documentation.
NLPModelMeta objects have the following attributes (with
S <: AbstractVector):
|number of variables|
|vector of lower bounds|
|vector of upper bounds|
|indices of fixed variables|
|indices of variables with lower bound only|
|indices of variables with upper bound only|
|indices of variables with lower and upper bound (range)|
|indices of free variables|
|indices of visibly infeasible bounds|
|total number of general constraints|
|number of linear constraints|
|number of nonlinear general constraints|
|initial Lagrange multipliers|
|vector of constraint lower bounds|
|vector of constraint upper bounds|
|indices of linear constraints|
|indices of nonlinear constraints|
|indices of equality constraints|
|indices of constraints of the form c(x) ≥ cl|
|indices of constraints of the form c(x) ≤ cu|
|indices of constraints of the form cl ≤ c(x) ≤ cu|
|indices of "free" constraints (there shouldn't be any)|
|indices of the visibly infeasible constraints|
|number of nonzeros in the gradient|
|number of nonzeros in the sparse Jacobian|
|number of nonzeros in the sparse Hessian|
|true if |
|true if the problem is a linear program|
Bug reports and discussions
If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.
If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.
Source Code: https://github.com/JuliaSmoothOptimizers/NLPModels.jl
License: View license
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